EuroSciPy<p>Developing Bayesian inference methods for complex scientific problems?</p><p><a href="https://fosstodon.org/tags/EuroSciPy2025" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>EuroSciPy2025</span></a> is seeking original work on Hamiltonian Monte Carlo, variational inference, and statistical modeling in <a href="https://fosstodon.org/tags/Python" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Python</span></a>.</p><p>Submit your innovations: <a href="https://pretalx.com/euroscipy-2025/cfp" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="">pretalx.com/euroscipy-2025/cfp</span><span class="invisible"></span></a> <a href="https://fosstodon.org/tags/CfP" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>CfP</span></a></p><p><a href="https://fosstodon.org/tags/BayesianStatistics" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>BayesianStatistics</span></a> <a href="https://fosstodon.org/tags/ScientificPython" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ScientificPython</span></a> <a href="https://fosstodon.org/tags/BayesianInference" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>BayesianInference</span></a> <a href="https://fosstodon.org/tags/PyMC" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>PyMC</span></a> <a href="https://fosstodon.org/tags/PyStan" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>PyStan</span></a> <a href="https://fosstodon.org/tags/EuroSciPy" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>EuroSciPy</span></a></p>
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In the Dark<p><strong>Weekly Update at the Open Journal of Astrophysics – 08/03/2025</strong></p><p>Time for the weekly Saturday morning update of papers published at the <a href="https://astro.theoj.org" rel="nofollow noopener" target="_blank">Open Journal of Astrophysics</a>. Since the <a href="https://telescoper.blog/2025/03/01/weekly-update-from-the-open-journal-of-astrophysics-01-03-2025/" rel="nofollow noopener" target="_blank">last update </a>we have published four new papers, which brings the number in <a href="https://astro.theoj.org/issue/11229" rel="nofollow noopener" target="_blank">Volume 8 (2025)</a> up to 25 and the total so far published by OJAp up to 260.</p><p>In chronological order of publication, the four papers published this week, with their overlays, are as follows. You can click on the images of the overlays to make them larger should you wish to do so.</p><p>The first paper to report is “<a href="https://astro.theoj.org/article/131858-partition-function-approach-to-non-gaussian-likelihoods-information-theory-and-state-variables-for-bayesian-inference" rel="nofollow noopener" target="_blank">Partition function approach to non-Gaussian likelihoods: information theory and state variables for Bayesian inference</a>” by Rebecca Maria Kuntz, Heinrich von Campe, Tobias Röspel, Maximilian Philipp Herzog, and Björn Malte Schäfer, all from the University of Heidelberg (Germany). It was published on Wednesday March 5th 2025 in the folder Cosmology and NonGalactic Astrophysics and it discusses the relationship between information theory and thermodynamics with applications to Bayesian inference in the context of cosmological data sets.</p><p><a href="https://telescoper.wordpress.com/wp-content/uploads/2025/03/kuntz_overlay.jpg" rel="nofollow noopener" target="_blank"></a></p><p> </p><p>You can read the officially accepted version of this paper on arXiv <a href="https://arxiv.org/abs/2411.13625v2" rel="nofollow noopener" target="_blank">here</a>.</p><p>The second paper of the week is “<a href="https://astro.theoj.org/article/131902-the-cosmological-population-of-gamma-ray-bursts-from-the-disks-of-active-galactic-nuclei" rel="nofollow noopener" target="_blank">The Cosmological Population of Gamma-Ray Bursts from the Disks of Active Galactic Nuclei</a>” by Hoyoung D. Kang & Rosalba Perna (Stony Brook), Davide Lazzati (Oregon State), and Yi-Han Wang (U. Nevada), all based in the USA. It was published on Thursday 6th March 2025 in the folder <a href="https://astro.theoj.org/section/1191-high-energy-astrophysical-phenomena" rel="nofollow noopener" target="_blank">High-Energy Astrophysical Phenomena</a>. The authors use models for GRB electromagnetic emission to simulate the cosmological occurrence and observational detectability of both long and short GRBs within AGN disks</p><p><a href="https://telescoper.wordpress.com/wp-content/uploads/2025/03/kang_overlay.jpg" rel="nofollow noopener" target="_blank"></a></p><p>You can find the officially accepted version of this paper on arXiv <a href="https://arxiv.org/abs/2412.17714v2" rel="nofollow noopener" target="_blank">here</a>.</p><p>The next two papers were published on Friday 7th March 2025.</p><p>“<a href="https://astro.theoj.org/article/131968-the-distribution-of-misalignment-angles-in-multipolar-planetary-nebulae" rel="nofollow noopener" target="_blank">The distribution of misalignment angles in multipolar planetary nebulae</a>” by Ido Avitan and Noam Soker (Technion, Haifa, Israel) analyzes the statistics of measured misalignment angles in multipolar planetary nebulae implies a random three-dimensional angle distribution limited to <60 degrees. It is in the folder <a href="https://astro.theoj.org/section/1193-solar-and-stellar-astrophysics" rel="nofollow noopener" target="_blank">Solar and Stellar Astrophysics</a>.</p><p>Here is the overlay:</p><p><a href="https://telescoper.wordpress.com/wp-content/uploads/2025/03/avitan_overlay.jpg" rel="nofollow noopener" target="_blank"></a></p><p> </p><p>The official published version can be found on the arXiv <a href="https://arxiv.org/abs/2501.04549v2" rel="nofollow noopener" target="_blank">here</a>.</p><p>The last paper to report this week is “The DESI-Lensing Mock Challenge: large-scale cosmological analysis of 3×2-pt statistics” by Chris Blake (Swinburne, Australia) and 43 others; this is a large international collaboration and I apologize for not being able to list all the authors here!</p><p>This one is in the folder marked Cosmology and NonGalactic Astrophysics; it presents an end-to-end simulation study designed to test the analysis pipeline for the Dark Energy Spectroscopic Instrument (DESI) Year 1 galaxy redshift dataset combined with weak gravitational lensing from other surveys.</p><p>The overlay is here:</p><p><a href="https://telescoper.wordpress.com/wp-content/uploads/2025/03/blake_2_overlay.jpg" rel="nofollow noopener" target="_blank"></a></p><p> </p><p>You can find the “final” version on arXiv <a href="https://arxiv.org/abs/2412.12548v2" rel="nofollow noopener" target="_blank">here</a>.</p><p>That’s all for this week. It’s good to see such an interesting variety of topics. I’ll do another update next Saturday</p><p><a rel="nofollow noopener" class="hashtag u-tag u-category" href="https://telescoper.blog/tag/3x2pt-analysis/" target="_blank">#3x2ptAnalysis</a> <a rel="nofollow noopener" class="hashtag u-tag u-category" href="https://telescoper.blog/tag/active-galactic-nuclei/" target="_blank">#ActiveGalacticNuclei</a> <a rel="nofollow noopener" class="hashtag u-tag u-category" href="https://telescoper.blog/tag/arxiv2411-13625v2/" target="_blank">#arXiv241113625v2</a> <a rel="nofollow noopener" class="hashtag u-tag u-category" href="https://telescoper.blog/tag/arxiv2412-12548v2/" target="_blank">#arXiv241212548v2</a> <a rel="nofollow noopener" class="hashtag u-tag u-category" href="https://telescoper.blog/tag/arxiv2412-17714v2/" target="_blank">#arXiv241217714v2</a> <a rel="nofollow noopener" class="hashtag u-tag u-category" href="https://telescoper.blog/tag/arxiv2501-04549v2/" target="_blank">#arXiv250104549v2</a> <a rel="nofollow noopener" class="hashtag u-tag u-category" href="https://telescoper.blog/tag/bayesian-inference/" target="_blank">#BayesianInference</a> <a rel="nofollow noopener" class="hashtag u-tag u-category" href="https://telescoper.blog/tag/cosmology/" target="_blank">#Cosmology</a> <a rel="nofollow noopener" class="hashtag u-tag u-category" href="https://telescoper.blog/tag/cosmology-and-nongalactic-astrophysics/" target="_blank">#CosmologyAndNonGalacticAstrophysics</a> <a rel="nofollow noopener" class="hashtag u-tag u-category" href="https://telescoper.blog/tag/desi/" target="_blank">#DESI</a> <a rel="nofollow noopener" class="hashtag u-tag u-category" href="https://telescoper.blog/tag/diamond-open-access/" target="_blank">#DiamondOpenAccess</a> <a rel="nofollow noopener" class="hashtag u-tag u-category" href="https://telescoper.blog/tag/diamond-open-access-publishing/" target="_blank">#DiamondOpenAccessPublishing</a> <a rel="nofollow noopener" class="hashtag u-tag u-category" href="https://telescoper.blog/tag/entropy/" target="_blank">#entropy</a> <a rel="nofollow noopener" class="hashtag u-tag u-category" href="https://telescoper.blog/tag/gamma-ray-bursts/" target="_blank">#GammaRayBursts</a> <a rel="nofollow noopener" class="hashtag u-tag u-category" href="https://telescoper.blog/tag/high-energy-astrophysical-phenomena/" target="_blank">#HighEnergyAstrophysicalPhenomena</a> <a rel="nofollow noopener" class="hashtag u-tag u-category" href="https://telescoper.blog/tag/information-theory/" target="_blank">#InformationTheory</a> <a rel="nofollow noopener" class="hashtag u-tag u-category" href="https://telescoper.blog/tag/numerical-simulations/" target="_blank">#numericalSimulations</a> <a rel="nofollow noopener" class="hashtag u-tag u-category" href="https://telescoper.blog/tag/planetary-nebulae/" target="_blank">#planetaryNebulae</a> <a rel="nofollow noopener" class="hashtag u-tag u-category" href="https://telescoper.blog/tag/solar-and-stellar-astrophysics/" target="_blank">#SolarAndStellarAstrophysics</a> <a rel="nofollow noopener" class="hashtag u-tag u-category" href="https://telescoper.blog/tag/statistical-mechanics/" target="_blank">#StatisticalMechanics</a> <a rel="nofollow noopener" class="hashtag u-tag u-category" href="https://telescoper.blog/tag/weak-lensing/" target="_blank">#WeakLensing</a></p>
Peter McMahan<p>I'm explaining Hamiltonian Monte Carlo in my grad-level stats class tomorrow, so I put together this animation illustrating HMC in one dimension. I find it very soothing.</p><p><a href="https://mas.to/tags/bayesian" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>bayesian</span></a> <a href="https://mas.to/tags/BayesianInference" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>BayesianInference</span></a> <a href="https://mas.to/tags/posterior" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>posterior</span></a> <a href="https://mas.to/tags/stats" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>stats</span></a> <a href="https://mas.to/tags/r" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>r</span></a> <a href="https://mas.to/tags/rlang" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>rlang</span></a> <a href="https://mas.to/tags/statistics" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>statistics</span></a> <a href="https://mas.to/tags/MCMC" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>MCMC</span></a></p>
Dr. Anna Latour<p>I'm teaching my first lecture at the new job today, about probabilistic logic programming, probabilistic inference, and (weighted) model counting.</p><p>Some of the required reading is a paper (<a href="https://eccc.weizmann.ac.il/eccc-reports/2003/TR03-003/index.html" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">eccc.weizmann.ac.il/eccc-repor</span><span class="invisible">ts/2003/TR03-003/index.html</span></a>) that was written by a great mentor of mine, prof. dr. Fahiem Bacchus. He passed away just over 2 years ago, and I am honoured to keep his memory alive by teaching his ideas to a new generation of students. Hope to do him proud. 🌱 </p><p>Please send good vibes? 🥺 </p><p><a href="https://mathstodon.xyz/tags/AcademicChatter" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>AcademicChatter</span></a> <a href="https://mathstodon.xyz/tags/AcademicLife" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>AcademicLife</span></a> <a href="https://mathstodon.xyz/tags/AcademicMastodon" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>AcademicMastodon</span></a> <a href="https://mathstodon.xyz/tags/Teaching" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Teaching</span></a> <a href="https://mathstodon.xyz/tags/Probability" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Probability</span></a> <a href="https://mathstodon.xyz/tags/ProbabilisticInference" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ProbabilisticInference</span></a> <a href="https://mathstodon.xyz/tags/Probabilities" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Probabilities</span></a> <a href="https://mathstodon.xyz/tags/Logic" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Logic</span></a> <a href="https://mathstodon.xyz/tags/LogicProgramming" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>LogicProgramming</span></a> <a href="https://mathstodon.xyz/tags/PropositionalModelCounting" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>PropositionalModelCounting</span></a> <a href="https://mathstodon.xyz/tags/ProbabilisticLogicProgramming" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ProbabilisticLogicProgramming</span></a> <a href="https://mathstodon.xyz/tags/ModelCounting" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ModelCounting</span></a> <a href="https://mathstodon.xyz/tags/PropositionalLogic" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>PropositionalLogic</span></a> <a href="https://mathstodon.xyz/tags/WeightedModelCounting" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>WeightedModelCounting</span></a> <a href="https://mathstodon.xyz/tags/DPLL" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>DPLL</span></a> <a href="https://mathstodon.xyz/tags/BayesianProbability" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>BayesianProbability</span></a> <a href="https://mathstodon.xyz/tags/BayesNets" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>BayesNets</span></a> <a href="https://mathstodon.xyz/tags/BasianStatistics" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>BasianStatistics</span></a> <a href="https://mathstodon.xyz/tags/BayesianInference" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>BayesianInference</span></a> <a href="https://mathstodon.xyz/tags/BayesianNetworks" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>BayesianNetworks</span></a> <a href="https://mathstodon.xyz/tags/KnowledgeCompilation" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>KnowledgeCompilation</span></a> <a href="https://mathstodon.xyz/tags/DecisionDiagrams" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>DecisionDiagrams</span></a> <a href="https://mathstodon.xyz/tags/BinaryDecisionDiagrams" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>BinaryDecisionDiagrams</span></a></p>
In the Dark<p><strong>Two New Publications at the Open Journal of Astrophysics</strong></p><p>It’s Saturday morning again so here’s another report on activity at the Open Journal of Astrophysics. Since <a href="https://telescoper.blog/2024/10/19/six-new-publications-at-the-open-journal-of-astrophysics-2/" rel="nofollow noopener" target="_blank">the last update</a> we have published two more papers, taking the count in <a href="https://astro.theoj.org/issue/8655" rel="nofollow noopener" target="_blank">Volume 7 (2024)</a> up to 95 and the total published by OJAp up to 210. We’ve still got a few in the pipeline waiting for the final versions to appear on arXiv so I expect we’ll reach the 100 mark for 2024 in the next couple of weeks.</p><p>The<a href="https://astro.theoj.org/article/123239-massive-black-hole-seeds" rel="nofollow noopener" target="_blank"> first paper</a> of the most recent pair, published on October 22 2024, and in the folder marked <a href="https://astro.theoj.org/section/1189-astrophysics-of-galaxies" rel="nofollow noopener" target="_blank">Astrophysics of Galaxies</a>, is “<a href="https://astro.theoj.org/article/124112-cloud-collision-signatures-in-the-central-molecular-zone" rel="nofollow noopener" target="_blank">Cloud Collision Signatures in the Central Molecular Zone</a>” by Rees A. Barnes and Felix D. Priestley (Cardiff University, UK) . This paper presents an analysis of combined hydrodynamical, chemical and radiative transfer simulations of cloud collisions in the Galactic disk and Central Molecular Zone (CMZ).</p><p>Here is a screen grab of the overlay which includes the abstract:</p><p><a href="https://telescoper.blog/wp-content/uploads/2024/10/barnes_overlay.jpg" rel="nofollow noopener" target="_blank"></a></p><p> </p><p> </p><p>You can click on the image of the overlay to make it larger should you wish to do so. You can find the officially accepted version of this paper on the arXiv <a href="https://arxiv.org/abs/2407.21575v2" rel="nofollow noopener" target="_blank">here</a>.</p><p>The <a href="https://astro.theoj.org/article/123368-the-future-of-cosmological-likelihood-based-inference-accelerated-high-dimensional-parameter-estimation-and-model-comparison" rel="nofollow noopener" target="_blank">second paper </a>has the title “Partition function approach to non-Gaussian likelihoods: macrocanonical partitions and replicating Markov-chains” and was published October 25th 2024. The authors are Maximilian Philipp Herzog, Heinrich von Campe, Rebecca Maria Kuntz, Lennart Röver and Björn Malte Schäfe (all of Heidelberg University, Germany). This paper, which is in the folder marked<a href="https://astro.theoj.org/section/1188-cosmology-and-nongalactic-astrophysics" rel="nofollow noopener" target="_blank"> Cosmology and NonGalactic Astrophysics</a>, describes a method of macrocanonical sampling for Bayesian statistical inference, based on the macrocanonical partition function, with applications to cosmology.</p><p>Here is a screen grab of the overlay which includes the abstract:</p><p> </p><p><a href="https://telescoper.blog/wp-content/uploads/2024/10/herzog_overlay-1.jpg" rel="nofollow noopener" target="_blank"></a></p><p>You can click on the image of the overlay to make it larger should you wish to do so. You can find the officially accepted version of the paper on the arXiv <a href="https://arxiv.org/abs/2311.16218v3" rel="nofollow noopener" target="_blank">here</a>.</p><p>That concludes this week’s update. More next week!</p><p><a rel="nofollow noopener" class="hashtag u-tag u-category" href="https://telescoper.blog/tag/arxiv2311-16218v3/" target="_blank">#arXiv231116218v3</a> <a rel="nofollow noopener" class="hashtag u-tag u-category" href="https://telescoper.blog/tag/arxiv2407-21575v2/" target="_blank">#arXiv240721575v2</a> <a rel="nofollow noopener" class="hashtag u-tag u-category" href="https://telescoper.blog/tag/astrophysics-of-galaxies/" target="_blank">#AstrophysicsOfGalaxies</a> <a rel="nofollow noopener" class="hashtag u-tag u-category" href="https://telescoper.blog/tag/bayesian-inference/" target="_blank">#BayesianInference</a> <a rel="nofollow noopener" class="hashtag u-tag u-category" href="https://telescoper.blog/tag/cosmology-and-nongalactic-astrophysics/" target="_blank">#CosmologyAndNonGalacticAstrophysics</a> <a rel="nofollow noopener" class="hashtag u-tag u-category" href="https://telescoper.blog/tag/likelihoods/" target="_blank">#likelihoods</a> <a rel="nofollow noopener" class="hashtag u-tag u-category" href="https://telescoper.blog/tag/markov-chains/" target="_blank">#MarkovChains</a> <a rel="nofollow noopener" class="hashtag u-tag u-category" href="https://telescoper.blog/tag/molecular-couds/" target="_blank">#MolecularCouds</a> <a rel="nofollow noopener" class="hashtag u-tag u-category" href="https://telescoper.blog/tag/partition-function/" target="_blank">#PartitionFunction</a> <a rel="nofollow noopener" class="hashtag u-tag u-category" href="https://telescoper.blog/tag/star-formation/" target="_blank">#starFormation</a> <a rel="nofollow noopener" class="hashtag u-tag u-category" href="https://telescoper.blog/tag/thermodynamics/" target="_blank">#thermodynamics</a></p>
In the Dark<p>It’s Saturday morning again so here’s another report on activity at the Open Journal of Astrophysics. Since <a href="https://telescoper.blog/2024/08/31/two-new-publications-at-the-open-journal-of-astrophysics-14/" rel="nofollow noopener" target="_blank">the last update</a> we have published two more papers, taking the count in <a href="https://astro.theoj.org/issue/8655" rel="nofollow noopener" target="_blank">Volume 7 (2024)</a> up to 73 and the total published by OJAp up to 188. We’ve still got a few in the pipeline waiting for the final versions to appear on arXiv so I expect we’ll reach the 200 mark fairly soon.</p><p>The<a href="https://astro.theoj.org/article/123239-massive-black-hole-seeds" rel="nofollow noopener" target="_blank"> first paper</a> of the most recent pair, published on September 4th 2024, and in the folder marked <a href="https://astro.theoj.org/section/1189-astrophysics-of-galaxies" rel="nofollow noopener" target="_blank">Astrophysics of Galaxies</a>, is “Massive Black Hole Seeds” by John Regan of the Department of <del>Theoretical</del> Physics at Maynooth University and Marta Volonteri (Sorbonne Université, Paris, France). This article presents a discussion of the pathways to the formation of massive black holes, including both light and heavy initial seeds.</p><p>Here is a screen grab of the overlay which includes the abstract:</p><p><a href="https://telescoper.blog/wp-content/uploads/2024/09/regan_glitch.jpg" rel="nofollow noopener" target="_blank"></a></p><p> </p><p> </p><p>You can click on the image of the overlay to make it larger should you wish to do so. <del>Those of you who are paying attention will see that there is a bit of a glitch on the left hand side where software has thrown a line break in between the two author names. I have no idea what caused this so I raised a ticket with Scholastica and no doubt it will soon be fixed</del>. (<em>Update: it is now fixed, 12th September 2024</em>). You can find the officially accepted version of this paper on the arXiv <a href="https://arxiv.org/abs/2405.17975v2" rel="nofollow noopener" target="_blank">here</a>.</p><p>The <a href="https://astro.theoj.org/article/123368-the-future-of-cosmological-likelihood-based-inference-accelerated-high-dimensional-parameter-estimation-and-model-comparison" rel="nofollow noopener" target="_blank">second paper </a>has the title “The future of cosmological likelihood-based inference: accelerated high-dimensional parameter estimation and model comparison” and was published on 5th September 2024. The authors are Davide Piras (Université de Genève), Alicja Polanska (MSSL) , Alessio Spurio Mancini (Royal Holloway, London), Matthew A. Price(UCL) & Jason D. McEwen (UCL); the latter four are all based in the UK. This paper, which is in the folder marked<a href="https://astro.theoj.org/section/1188-cosmology-and-nongalactic-astrophysics" rel="nofollow noopener" target="_blank"> Cosmology and NonGalactic Astrophysics</a>, describes an accelerated approach to Bayesian inference in higher-dimensional settings, as required for cosmology, based on recent developments in machine learning and its underlying technology.</p><p>Here is a screen grab of the overlay which includes the abstract:</p><p><a href="https://telescoper.blog/wp-content/uploads/2024/09/piras_overlay.jpg" rel="nofollow noopener" target="_blank"></a></p><p> </p><p> </p><p>You can click on the image of the overlay to make it larger should you wish to do so. You can find the officially accepted version of the paper on the arXiv <a href="https://arxiv.org/abs/2405.12965v2" rel="nofollow noopener" target="_blank">here</a>.</p><p>That concludes this week’s update. More next week!</p><p><a href="https://telescoper.blog/2024/09/07/two-new-publications-at-the-open-journal-of-astrophysics-15/" class="" rel="nofollow noopener" target="_blank">https://telescoper.blog/2024/09/07/two-new-publications-at-the-open-journal-of-astrophysics-15/</a></p><p><a rel="nofollow noopener" class="hashtag u-tag u-category" href="https://telescoper.blog/tag/arxiv2405-12965v2/" target="_blank">#arXiv240512965v2</a> <a rel="nofollow noopener" class="hashtag u-tag u-category" href="https://telescoper.blog/tag/astrophysics-of-galaxies/" target="_blank">#AstrophysicsOfGalaxies</a> <a rel="nofollow noopener" class="hashtag u-tag u-category" href="https://telescoper.blog/tag/bayesian-inference/" target="_blank">#BayesianInference</a> <a rel="nofollow noopener" class="hashtag u-tag u-category" href="https://telescoper.blog/tag/black-hole-seeds/" target="_blank">#BlackHoleSeeds</a> <a rel="nofollow noopener" class="hashtag u-tag u-category" href="https://telescoper.blog/tag/black-holes/" target="_blank">#blackHoles</a> <a rel="nofollow noopener" class="hashtag u-tag u-category" href="https://telescoper.blog/tag/cosmology-and-nongalactic-astrophysics/" target="_blank">#CosmologyAndNonGalacticAstrophysics</a> <a rel="nofollow noopener" class="hashtag u-tag u-category" href="https://telescoper.blog/tag/diamond-open-access/" target="_blank">#DiamondOpenAccess</a> <a rel="nofollow noopener" class="hashtag u-tag u-category" href="https://telescoper.blog/tag/likelihood-based-inference/" target="_blank">#likelihoodBasedInference</a> <a rel="nofollow noopener" class="hashtag u-tag u-category" href="https://telescoper.blog/tag/open-journal-of-astrophysics/" target="_blank">#OpenJournalOfAstrophysics</a> <a rel="nofollow noopener" class="hashtag u-tag u-category" href="https://telescoper.blog/tag/the-open-journal-of-astrophysics/" target="_blank">#TheOpenJournalOfAstrophysics</a></p>
Martin Modrák<p>New on the blog: showcasing the immense hackability of <a href="https://fediscience.org/tags/brms" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>brms</span></a> by extending a random intercept model with linear predictors on the standard deviation of the random intercept. Should you do it? Most likely not, but if you really really want, there is a way. Also the techniques shown are general and let you do a lot of other crazy stuff with brms. Happy for any feedback!<br><a href="https://www.martinmodrak.cz/2024/02/17/brms-hacking-linear-predictors-for-random-effect-standard-deviations/" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://www.</span><span class="ellipsis">martinmodrak.cz/2024/02/17/brm</span><span class="invisible">s-hacking-linear-predictors-for-random-effect-standard-deviations/</span></a></p><p><a href="https://fediscience.org/tags/bayesian" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>bayesian</span></a> <a href="https://fediscience.org/tags/BayesianStatistics" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>BayesianStatistics</span></a> <a href="https://fediscience.org/tags/BayesianInference" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>BayesianInference</span></a> <a href="https://fediscience.org/tags/MixedModels" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>MixedModels</span></a></p>
CJ Stevens - Metaphysiology<p>Who'd like to work out the "likelihood" that all this is simply coincidence? 😛</p><p>William <a href="https://mastodon.social/tags/Blake" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Blake</span></a> died in 1827, the same year as Pierre-Simon <a href="https://mastodon.social/tags/LaPlace" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>LaPlace</span></a>, and is buried in the same cemetery as Thomas <a href="https://mastodon.social/tags/Bayes" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Bayes</span></a>. In 1788 Blake wrote this:</p><p>"...the ratio of all we have already known, is not the same that it shall be when we know more."</p><p><a href="https://mastodon.social/tags/BayesianInference" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>BayesianInference</span></a><br><a href="https://mastodon.social/tags/InverseProbability" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>InverseProbability</span></a><br><a href="https://mastodon.social/tags/WilliamBlake" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>WilliamBlake</span></a><br><a href="https://mastodon.social/tags/PierreSimonLaPlace" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>PierreSimonLaPlace</span></a><br><a href="https://mastodon.social/tags/ThomasBayes" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ThomasBayes</span></a> <br><a href="https://mastodon.social/tags/Statistics" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Statistics</span></a></p>
Dominic Boutet<p>After a long break, new <a href="https://neuromatch.social/tags/arxivfeed" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>arxivfeed</span></a> </p><p>"Generalized Bayesian Inference for Scientific Simulators via Amortized Cost Estimation"<br><a href="https://arxiv.org/abs/2305.15208" rel="nofollow noopener" target="_blank"><span class="invisible">https://</span><span class="">arxiv.org/abs/2305.15208</span><span class="invisible"></span></a> </p><p><a href="https://neuromatch.social/tags/BayesianInference" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>BayesianInference</span></a> <a href="https://neuromatch.social/tags/MachineLearning" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>MachineLearning</span></a> <a href="https://neuromatch.social/tags/Modelling" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Modelling</span></a> <a href="https://neuromatch.social/tags/SBI" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>SBI</span></a> <a href="https://neuromatch.social/tags/ComputationalNeuroscience" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ComputationalNeuroscience</span></a></p>
Malte Ziebarth<p>Happy to share that the second paper of my PhD is now available as preprint and open for public discussion:<br><a href="https://doi.org/10.5194/egusphere-2023-222" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">doi.org/10.5194/egusphere-2023</span><span class="invisible">-222</span></a></p><p>We developed a stochastic model of regional surface heat flow and Bayesian methods for its quantification. In particular, we aim to infer the strength of a specifically shaped signal given a sample of heat flow measurements.<br><a href="https://norden.social/tags/geophysics" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>geophysics</span></a> <a href="https://norden.social/tags/heatflow" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>heatflow</span></a> <a href="https://norden.social/tags/openscience" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>openscience</span></a> <a href="https://norden.social/tags/BayesianInference" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>BayesianInference</span></a></p>
Vincent Voelz<p>Our new paper is now out in <a href="https://mas.to/tags/JCIM" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>JCIM</span></a> ! </p><p>BICePs v2.0: Software for Ensemble Reweighting Using Bayesian Inference of Conformational Populations</p><p><a href="https://doi.org/10.1021/acs.jcim.2c01296" rel="nofollow noopener" target="_blank"><span class="invisible">https://</span><span class="ellipsis">doi.org/10.1021/acs.jcim.2c012</span><span class="invisible">96</span></a> </p><p>Congrats to Rob Raddi on this paper and for coding this more user-friendly and extensible Python package <a href="https://mas.to/tags/BayesianInference" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>BayesianInference</span></a> <a href="https://mas.to/tags/compchem" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>compchem</span></a> <a href="https://mas.to/tags/chemistry" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>chemistry</span></a> <a href="https://mas.to/tags/biophysics" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>biophysics</span></a></p>
Ranjith Jaganathan<p>"Dear all,</p><p>We are thrilled to announce the inaugural <a href="https://neuromatch.social/tags/ComputationalPsychiatry" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ComputationalPsychiatry</span></a> Conference to take place at Trinity College Dublin on July 6-8th, 2023 (<a href="https://neuromatch.social/tags/cpconf2023" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>cpconf2023</span></a>) </p><p><a href="https://www.cpconf.org/" rel="nofollow noopener" target="_blank"><span class="invisible">https://www.</span><span class="">cpconf.org/</span><span class="invisible"></span></a></p><p>One of the key aims of <a href="https://neuromatch.social/tags/ComputationalNeuroscience" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ComputationalNeuroscience</span></a> is to construct theoretical accounts of normal mental function that link characterizations of <a href="https://neuromatch.social/tags/neurobiology" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>neurobiology</span></a>, <a href="https://neuromatch.social/tags/psychology" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>psychology</span></a> and aspects of the environment. In Computational Psychiatry (CP), these theories, realized in models at various scales, are used to elucidate dysfunction. </p><p>The 2023 Computational Psychiatry Conference (7th and 8th July) will contain six sessions, each with a keynote talk from senior faculty and also contributed talks and panel discussions. </p><p>The session themes will include Diagnostics, Reinforcement Learning models, Individual-level prediction, Development, Animal models and Treatments. There will also be poster sessions on both days. </p><p>The tutorial session (afternoon of 6th July) will contain three introductory talks on <a href="https://neuromatch.social/tags/psychiatry" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>psychiatry</span></a> for non-clinicians, <a href="https://neuromatch.social/tags/BehaviouralModelling" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>BehaviouralModelling</span></a> using <a href="https://neuromatch.social/tags/BayesianInference" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>BayesianInference</span></a> and <a href="https://neuromatch.social/tags/ReinforcementLearning" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ReinforcementLearning</span></a>, and <a href="https://neuromatch.social/tags/MachineLearning" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>MachineLearning</span></a>.</p><p>Abstract submissions will be closed on March 15th, 2023. We will be able to support 10 participants with a travel award based on a competitive review of their abstract submissions. Top submissions will also be invited as talks.</p><p>We look forward to seeing everyone in Dublin this summer!"</p>
Gianluca Detommaso<p>🚀 <a href="https://sigmoid.social/tags/AWS" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>AWS</span></a> Fortuna is skyrocketing! 🚀 Just a few days, and so many GitHub stars and forks! ⭐️</p><p>Fortuna supports <a href="https://sigmoid.social/tags/ConformalPrediction" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ConformalPrediction</span></a>, <a href="https://sigmoid.social/tags/BayesianInference" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>BayesianInference</span></a> and other methods for <a href="https://sigmoid.social/tags/UncertaintyQuantification" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>UncertaintyQuantification</span></a> in <a href="https://sigmoid.social/tags/DeepLearning" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>DeepLearning</span></a>. </p><p>Try it out and let us know! <br><a href="https://github.com/awslabs/fortuna" rel="nofollow noopener" target="_blank"><span class="invisible">https://</span><span class="">github.com/awslabs/fortuna</span><span class="invisible"></span></a></p><p>In collaboration with <span class="h-card"><a href="https://sigmoid.social/@cedapprox" class="u-url mention" rel="nofollow noopener" target="_blank">@<span>cedapprox</span></a></span>, <span class="h-card"><a href="https://sigmoid.social/@andrewgwils" class="u-url mention" rel="nofollow noopener" target="_blank">@<span>andrewgwils</span></a></span> and team. </p><p><a href="https://sigmoid.social/tags/uncertainty" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>uncertainty</span></a> <a href="https://sigmoid.social/tags/neuralnetworks" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>neuralnetworks</span></a> <a href="https://sigmoid.social/tags/bayesian" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>bayesian</span></a> <a href="https://sigmoid.social/tags/conformal" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>conformal</span></a> <a href="https://sigmoid.social/tags/calibration" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>calibration</span></a> <a href="https://sigmoid.social/tags/jax" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>jax</span></a> <a href="https://sigmoid.social/tags/flax" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>flax</span></a> <a href="https://sigmoid.social/tags/python" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>python</span></a> <a href="https://sigmoid.social/tags/opensource" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>opensource</span></a> <a href="https://sigmoid.social/tags/library" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>library</span></a> <a href="https://sigmoid.social/tags/machinelearning" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>machinelearning</span></a> <a href="https://sigmoid.social/tags/ai" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ai</span></a></p>
Marcel Fröhlich<p>"Our results show that a Bayesian machine can be implemented in a system with distributed <a href="https://sigmoid.social/tags/memristors" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>memristors</span></a>, performing computation<br>locally, and with min. energy movement, allowing the computation of <a href="https://sigmoid.social/tags/BayesianInference" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>BayesianInference</span></a> with an energy efficiency more than three orders of magnitude higher than a standard microcontroller unit. Due to its reliance on non-volatile memory, and its sole use of read ops, once [...] programmed, the system may be powered down anytime while regaining functionality instantly. "</p>
Cedric Archambeau<p>Today, we open sourced Fortuna (<a href="https://github.com/awslabs/fortuna" rel="nofollow noopener" target="_blank"><span class="invisible">https://</span><span class="">github.com/awslabs/fortuna</span><span class="invisible"></span></a>) a library for uncertainty quantification.<br>Deep neural networks are often overconfident and do not know what they don’t know. Quantifying the uncertainty in the predictions they make will help deploy deep learning more responsibly and more safely.<br><a href="https://sigmoid.social/tags/responsibleAI" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>responsibleAI</span></a> <a href="https://sigmoid.social/tags/ConformalPrediction" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ConformalPrediction</span></a> <a href="https://sigmoid.social/tags/BayesianInference" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>BayesianInference</span></a> <a href="https://sigmoid.social/tags/UncertaintyQuantification" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>UncertaintyQuantification</span></a> <a href="https://sigmoid.social/tags/deeplearning" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>deeplearning</span></a> <a href="https://sigmoid.social/tags/opensource" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>opensource</span></a></p>
Rob Zinkov<p>Don't forget to submit to the <a href="https://bayes.club/tags/PyMCon" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>PyMCon</span></a> web series</p><p>Submissions are due November 30th!</p><p>Details here: <a href="https://pymcon.com/cfp" rel="nofollow noopener" target="_blank"><span class="invisible">https://</span><span class="">pymcon.com/cfp</span><span class="invisible"></span></a></p><p>We'd love to receive your submission. Feel free to reach out with additional questions!</p><p>First-time speakers are especially encouraged to apply!</p><p><a href="https://bayes.club/tags/bayes" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>bayes</span></a> <a href="https://bayes.club/tags/BayesianInference" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>BayesianInference</span></a> <a href="https://bayes.club/tags/pymc" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>pymc</span></a></p>
Martin Trapp<p>Ok, I’m finally going start making a blog and writing posts about topics related to <a href="https://fediscience.org/tags/tractability" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>tractability</span></a> <a href="https://fediscience.org/tags/BayesianInference" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>BayesianInference</span></a> <a href="https://fediscience.org/tags/nonparametrics" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>nonparametrics</span></a> and <a href="https://fediscience.org/tags/deeplearing" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>deeplearing</span></a>.</p>
Solal Nathan<p>🤔 Bayesian Inference (on graphical models) is NP-hard.</p><p>But even worst! every epsilon-approximation is also NP-hard.</p><p>Which means that the worst case scenario is (almost certainly) exponential.</p><p>Good news is, there are some special cases where approximation or exact inference can be performed efficiently.</p><p>📘 Check out more in "Probabilistic Graphical Models: Principles and Technique" by Daphne Koller and Nir Friedman</p><p><a href="https://sigmoid.social/tags/Bayes" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Bayes</span></a> <a href="https://sigmoid.social/tags/bayesianism" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>bayesianism</span></a> <a href="https://sigmoid.social/tags/MachineLearning" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>MachineLearning</span></a> <a href="https://sigmoid.social/tags/AI" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>AI</span></a> <a href="https://sigmoid.social/tags/ML" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ML</span></a> <a href="https://sigmoid.social/tags/BayesianInference" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>BayesianInference</span></a> <a href="https://sigmoid.social/tags/Inference" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Inference</span></a></p>
Jim Donegan 🎵 ✅<p><a href="https://mastodon.scot/tags/AlisonGopnik" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>AlisonGopnik</span></a> - What is <a href="https://mastodon.scot/tags/Causation" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Causation</span></a> ? </p><p><a href="https://www.youtube.com/watch?v=m2ZKgiWM3JM&ab_channel=CloserToTruth" rel="nofollow noopener" target="_blank"><span class="invisible">https://www.</span><span class="ellipsis">youtube.com/watch?v=m2ZKgiWM3J</span><span class="invisible">M&ab_channel=CloserToTruth</span></a> </p><p><a href="https://mastodon.scot/tags/Philosophy" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Philosophy</span></a> <a href="https://mastodon.scot/tags/Science" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Science</span></a> <a href="https://mastodon.scot/tags/LawsOfPhysics" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>LawsOfPhysics</span></a> <a href="https://mastodon.scot/tags/LawsOfNature" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>LawsOfNature</span></a> <a href="https://mastodon.scot/tags/Correlation" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Correlation</span></a> <a href="https://mastodon.scot/tags/PhilosophyOfScience" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>PhilosophyOfScience</span></a> <a href="https://mastodon.scot/tags/Probability" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Probability</span></a> <a href="https://mastodon.scot/tags/BayesianInference" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>BayesianInference</span></a> <a href="https://mastodon.scot/tags/Bayesianism" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Bayesianism</span></a></p>
rowan_ 🐍<p>🚨 <a href="https://fosstodon.org/tags/inferentialstatistics" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>inferentialstatistics</span></a> 🚨</p><p>Call for proposals for the <a href="https://fosstodon.org/tags/PyMCon" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>PyMCon</span></a> web series is open!</p><p>What to propose? Papers, workshops, roundtables, demos, any engaging and unique formats you can think of.</p><p>🥇 First-time speakers are encouraged!<br>👀 The review process will be double-blind.<br>📍 Submissions are due Nov. 30. </p><p>Details here: <a href="https://pymcon.com/cfp" rel="nofollow noopener" target="_blank"><span class="invisible">https://</span><span class="">pymcon.com/cfp</span><span class="invisible"></span></a></p><p>We'd love to receive your submission. Feel free to reach out with additional questions!</p><p><a href="https://fosstodon.org/tags/bayes" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>bayes</span></a> <a href="https://fosstodon.org/tags/BayesianInference" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>BayesianInference</span></a> <a href="https://fosstodon.org/tags/inferenzstatistik" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>inferenzstatistik</span></a> <a href="https://fosstodon.org/tags/Bayesienne" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Bayesienne</span></a> <span class="h-card"><a href="https://bayes.club/@pymc" class="u-url mention" rel="nofollow noopener" target="_blank">@<span>pymc</span></a></span></p>
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