Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!My Intuitive Bayes Online Courses1:1 Mentorship with meIn this episode, we dive deep into gravitational wave astronomy, with Christopher Berry and John Veitch, two senior lecturers at the University of Glasgow and experts from the LIGO-VIRGO collaboration. They explain the significance of detecting gravitational waves, which are essential for understanding black holes and neutron stars collisions. This research not only sheds light on these distant events but also helps us grasp the fundamental workings of the universe.Our discussion focuses on the integral role of Bayesian statistics, detailing how they use nested sampling for extracting crucial information from the subtle signals of gravitational waves. This approach is vital for parameter estimation and understanding the distribution of cosmic sources through population inferences.Concluding the episode, Christopher and John highlight the latest advancements in black hole astrophysics and tests of general relativity, and touch upon the exciting prospects and challenges of the upcoming space-based LISA mission.Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !Thank you to my Patrons for making this episode possible!Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Scott Anthony Robson, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Andreas Kröpelin, Raphaël R, Nicolas Rode, Gabriel Stechschulte, Arkady, Kurt TeKolste, Gergely Juhasz, Marcus Nölke, Maggi Mackintosh, Grant Pezzolesi, Avram Aelony, Joshua Meehl, Javier Sabio, Kristian Higgins, Alex Jones, Gregorio Aguilar, Matt Rosinski, Bart Trudeau, Luis Fonseca, Dante Gates, Matt Niccolls, Maksim Kuznecov, Michael Thomas, Luke Gorrie, Cory Kiser and Julio.Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)Takeaways:  ⁃    Gravitational wave analysis involves using Bayesian statistics for parameter estimation and population inference.    ⁃    Nested sampling is a powerful algorithm used in gravitational wave analysis to explore parameter space and calculate the evidence for model selection.    ⁃    Machine learning techniques, such as normalizing flows, can be integrated with nested sampling to improve efficiency and explore complex distributions.    ⁃    The LIGO-VIRGO collaboration operates gravitational wave detectors that measure distortions in space and time caused by black hole and neutron star collisions.    ⁃    Sources of noise in gravitational wave detection include laser noise, thermal noise, seismic motion, and gravitational coupling.    ⁃    The LISA mission is a space-based gravitational wave detector that aims to observe lower frequency gravitational waves and unlock new astrophysical phenomena.    ⁃    Space-based detectors like LISA can avoid the ground-based noise and observe a different part of the gravitational wave spectrum, providing new insights into the universe.    ⁃    The data analysis challenges for space-based detectors are complex, as they require fitting multiple sources simultaneously and dealing with overlapping signals.    ⁃    Gravitational wave observations have the potential to test general relativity, study the astrophysics of black holes and neutron stars, and provide insights into cosmology.Links from the show:Christopher’s’ website: https://cplberry.com/John’s website: https://www.veitch.me.uk/Christopher on GitHub: https://github.com/cplb/ John on GitHub: https://github.com/johnveitchChristopher on Linkedin: http://www.linkedin.com/in/cplberry John on Linkedin: https://www.linkedin.com/in/john-veitch-56772225/Christopher on Twitter: https://twitter.com/cplberryJohn on Twitter: https://twitter.com/johnveitchChristopher on Mastodon: https://mastodon.scot/@cplberry@mastodon.online John on Mastodon: https://mastodon.scot/@JohnVeitchLIGO website: https://www.ligo.org/LIGO Gitlab: https://git.ligo.org/users/sign_inGravitational Wave Open Science Center: https://gwosc.org/LIGO Caltech Lab: https://www.ligo.caltech.edu/page/ligo-dataExoplanet, python package for probabilistic modeling of time series data in astronomy: https://docs.exoplanet.codes/en/latest/Dynamic Nested Sampling with dynesty: https://dynesty.readthedocs.io/en/latest/dynamic.htmlNessai, Nested sampling with artificial intelligence: https://nessai.readthedocs.io/LBS #98 Fusing Statistical Physics, Machine Learning & Adaptive MCMC, with Marylou Gabrié: https://learnbayesstats.com/episode/98-fusing-statistical-physics-machine-learning-adaptive-mcmc-marylou-gabrie/bayeux, JAX models with state-of-the-art inference methods: https://jax-ml.github.io/bayeux/LBS #51 Bernoulli’s Fallacy & the Crisis of Modern Science, with Aubrey Clayton: https://learnbayesstats.com/episode/51-bernoullis-fallacy-crisis-modern-science-aubrey-clayton/Aubrey Clayton's Probability Theory Lectures based on E.T Jaynes book: https://www.youtube.com/playlist?list=PL9v9IXDsJkktefQzX39wC2YG07vw7DsQ_TranscriptThis is an automatic transcript and may therefore contain errors. Please get in touch if you're willing to correct them.

Exploring variational inference in reactive message passing for continuous posterior updates, efficacy in teaching statistics, commercializing research tools for industrial use, and trade-offs in patient inference architecture for real-time signal processing applications.

Exploring reactive message passing in Bayesian inference for real-time data scenarios with unknown structures, including applications like denoising speech and real-time position tracking systems. Discussing the unique characteristics of RX Infar, a Bayesian inference tool inspired by the free energy principle, and the efficiency and speed advantages of using Julia programming language.

Dmitry Bagaev discusses reactive message passing in Bayesian inference and the development of RxInfer.jl. He talks about the challenges and benefits, variational inference, and the trade-offs in architecture. Dmitry shares insights into his startup Lazy Dynamics and the future of automated Bayesian inference. Also, his background in Russia, extreme sports hobbies, and the user-friendliness of inference methods are discussed.

The podcast explores common misconceptions in quantum physics and Bayesian probability, dispelling biases. It also delves into the concept of subjective reality and the significance of context in comprehending information.

In this podcast, quantum physics expert Chris Ferrie explores the link between quantum physics and Bayesian statistics. They discuss the practical application of Bayesian statistics, the challenges faced in transitioning to the subjective interpretation of probability, and the benefits of building something from scratch to deepen understanding. Ferrie also shares insights on using Bayesian statistics in research and the usefulness of tools like Q infer for solving problems in quantum physics.

In this episode, Chris Ferrie, an Associate Professor at the Centre for Quantum Software and Information of the University of Technology Sydney, discusses the utility of Bayesian stats in quantum physics research and shares insights from his work as an author. They also talk about science communication, education, and a shocking revelation about Ant Man.

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!My Intuitive Bayes Online Courses1:1 Mentorship with meListen to the full episode: https://learnbayesstats.com/episode/98-fusing-statistical-physics-machine-learning-adaptive-mcmc-marylou-gabrie/Watch the interview: https://www.youtube.com/watch?v=vVqZ0WWXX7g Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !Thank you to my Patrons for making this episode possible!Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Scott Anthony Robson, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Andreas Kröpelin, Raphaël R, Nicolas Rode, Gabriel Stechschulte, Arkady, Kurt TeKolste, Gergely Juhasz, Marcus Nölke, Maggi Mackintosh, Grant Pezzolesi, Avram Aelony, Joshua Meehl, Javier Sabio, Kristian Higgins, Alex Jones, Gregorio Aguilar, Matt Rosinski, Bart Trudeau, Luis Fonseca, Dante Gates, Matt Niccolls, Maksim Kuznecov, Michael Thomas, Luke Gorrie and Cory Kiser.Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!My Intuitive Bayes Online Courses1:1 Mentorship with meListen to the full episode: https://learnbayesstats.com/episode/98-fusing-statistical-physics-machine-learning-adaptive-mcmc-marylou-gabrie/Watch the interview: https://www.youtube.com/watch?v=vVqZ0WWXX7g Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !Thank you to my Patrons for making this episode possible!Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Scott Anthony Robson, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Andreas Kröpelin, Raphaël R, Nicolas Rode, Gabriel Stechschulte, Arkady, Kurt TeKolste, Gergely Juhasz, Marcus Nölke, Maggi Mackintosh, Grant Pezzolesi, Avram Aelony, Joshua Meehl, Javier Sabio, Kristian Higgins, Alex Jones, Gregorio Aguilar, Matt Rosinski, Bart Trudeau, Luis Fonseca, Dante Gates, Matt Niccolls, Maksim Kuznecov, Michael Thomas, Luke Gorrie and Cory Kiser.Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)

Marylou Gabrié, assistant professor at CMAP, Ecole Polytechnique in Paris, discusses the fusion of statistical physics and machine learning. Topics include machine learning for scientific computing, adaptive Monte Carlo with normalizing flows, sampling discrete parameters in generative models, and machine learning in scientific computing.