Shows
Learning Bayesian StatisticsAmortized Bayesian Inference with Deep Neural Networks, with Marvin SchmittProudly 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, Marvin Schmitt introduces the concept of amortized Bayesian inference, where the upfront training phase of a neural network is followed by fast posterior inference.Marvin will guide us through this new concept, discussing his work in probabilistic machine learning and uncertainty quantification, using Bayesian inference with deep neural networks. He also introduces BayesFlow, a Python library for amortized Bayesian workflows, and discusses its use cases in v...2024-05-291h 21Learning Bayesian StatisticsAmortized Bayesian Inference with Deep Neural Networks, with Marvin SchmittProudly 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, Marvin Schmitt introduces the concept of amortized Bayesian inference, where the upfront training phase of a neural network is followed by fast posterior inference.Marvin will guide us through this new concept, discussing his work in probabilistic machine learning and uncertainty quantification, using Bayesian inference with deep neural networks. He also introduces BayesFlow, a Python library for amortized Bayesian workflows, and discusses its use cases in v...2024-05-291h 21
Data & Science with Glen Wright ColopyEric Daza | Important Ideas in Causal Inference
Eric Daza | Important Ideas in Causal Inference
YouTube: https://youtu.be/K5nsSMJVIT0
Andrew Gelman and Aki Vehtari wrote a paper titled, "What are the most important statistical ideas of the past 50 years?". The first idea in the list is "counterfactual causal inference". Eric Daza (Evidation Health) walks us through the main ideas of the Gelman & Vehtari paper, drawing examples from several fields, including medical & healthcare statistics.
Topics
0:00 - Coming up...Correlation vs Causation
1:20 - Most important statistical ideas over the last 50 years
6:10 - Counterfactual Causal Inference2022-07-111h 23
Learning Bayesian StatisticsSilly & Empowering Statistics, with Chelsea Parlett-PelleritiYou wanna know something funny? A sentence from this episode became a meme. And people even made stickers out of it! Ok, that’s not true. But if someone could pull off something like that, it would surely be Chelsea Parlett-Pelleriti.Indeed, Chelsea’s research focuses on using statistics and machine learning on behavioral data, but her more general goal is to empower people to be able to do their own statistical analyses, through consulting, education, and, as you may have seen, stats memes on Twitter.A full-time teacher, researcher and statistical consultant, Chelsea earned an M...2021-08-301h 13Learning Bayesian StatisticsSilly & Empowering Statistics, with Chelsea Parlett-PelleritiYou wanna know something funny? A sentence from this episode became a meme. And people even made stickers out of it! Ok, that’s not true. But if someone could pull off something like that, it would surely be Chelsea Parlett-Pelleriti.Indeed, Chelsea’s research focuses on using statistics and machine learning on behavioral data, but her more general goal is to empower people to be able to do their own statistical analyses, through consulting, education, and, as you may have seen, stats memes on Twitter.A full-time teacher, researcher and statistical consultant, Chelsea earned an M...2021-08-301h 13
Department of StatisticsPractical pre-asymptotic diagnostic of Monte Carlo estimates in Bayesian inference and machine learningAki Vehtari (Aalto University) gives the OxCSML Seminar on Friday 7th May 2021 Abstract: I discuss the use of the Pareto-k diagnostic as a simple and practical approach for estimating both the required minimum sample size and empirical pre-asymptotic convergence rate for Monte Carlo estimates. Even when by construction a Monte Carlo estimate has finite variance the pre-asymptotic behaviour and convergence rate can be very different from the asymptotic behaviour following the central limit theorem. I demonstrate with practical examples in importance sampling, stochastic optimization, and variational inference, which are commonly used in Bayesian inference and machine learning.2021-07-2957 min
Learning Bayesian StatisticsThe Past, Present & Future of BRMS, with Paul BürknerEpisode sponsored by Tidelift: tidelift.comOne of the most common guest suggestions that you dear listeners make is… inviting Paul Bürkner on the show! Why? Because he’s a member of the Stan development team and he created BRMS, a popular R package to make and sample from Bayesian regression models using Stan. And, as I like you, I did invite Paul on the show and, well, that was a good call: we had an amazing conversation, spanning so many topics that I can’t list them all here!I asked him why he crea...2021-03-121h 07Learning Bayesian StatisticsThe Past, Present & Future of BRMS, with Paul BürknerEpisode sponsored by Tidelift: tidelift.comOne of the most common guest suggestions that you dear listeners make is… inviting Paul Bürkner on the show! Why? Because he’s a member of the Stan development team and he created BRMS, a popular R package to make and sample from Bayesian regression models using Stan. And, as I like you, I did invite Paul on the show and, well, that was a good call: we had an amazing conversation, spanning so many topics that I can’t list them all here!I asked him why he crea...2021-03-121h 07
Learning Bayesian Statistics#29 Model Assessment, Non-Parametric Models, And Much More, with Aki VehtariI’ll be honest here: I had a hard time summarizing this episode for you, and, let’s face it, it’s all my guest’s fault! Why? Because Aki Vehtari works on so many interesting projects that it’s hard to sum them all up, even more so because he was very generous with his time for this episode! But let’s try anyway, shall we?So, Aki is an Associate professor in computational probabilistic modeling at Aalto University, Finland. You already heard his delightful Finnish accent on episode 20, with Andrew Gelman and Jennifer Hill, talking about their...2020-12-021h 05Learning Bayesian Statistics#29 Model Assessment, Non-Parametric Models, And Much More, with Aki VehtariI’ll be honest here: I had a hard time summarizing this episode for you, and, let’s face it, it’s all my guest’s fault! Why? Because Aki Vehtari works on so many interesting projects that it’s hard to sum them all up, even more so because he was very generous with his time for this episode! But let’s try anyway, shall we?So, Aki is an Associate professor in computational probabilistic modeling at Aalto University, Finland. You already heard his delightful Finnish accent on episode 20, with Andrew Gelman and Jennifer Hill, talking about their...2020-12-021h 05
Learning Bayesian Statistics#27 Modeling the US Presidential Elections, with Andrew Gelman & Merlin HeidemannsIn a few days, a consequential election will take place, as citizens of the United States will go to the polls and elect their president — in fact they already started voting. You probably know a few forecasting models that try to predict what will happen on Election Day — who will get elected, by how much and with which coalition of States?But how do these statistical models work? How do you account for the different sources of uncertainty, be it polling errors, unexpected turnout or media events? How do you model covariation between States? How do you even...2020-11-011h 00Learning Bayesian Statistics#27 Modeling the US Presidential Elections, with Andrew Gelman & Merlin HeidemannsIn a few days, a consequential election will take place, as citizens of the United States will go to the polls and elect their president — in fact they already started voting. You probably know a few forecasting models that try to predict what will happen on Election Day — who will get elected, by how much and with which coalition of States?But how do these statistical models work? How do you account for the different sources of uncertainty, be it polling errors, unexpected turnout or media events? How do you model covariation between States? How do you even...2020-11-011h 00
Learning Bayesian Statistics#20 Regression and Other Stories, with Andrew Gelman, Jennifer Hill & Aki VehtariOnce upon a time, there was an enchanted book filled with hundreds of little plots, applied examples and linear regressions — the prettiest creature that was ever seen. Its authors were excessively fond of it, and its readers loved it even more. This magical book had a nice blue cover made for it, and everybody aptly called it « Regression and other Stories »!As every good fairy tale, this one had its share of villains — the traps where statistical methods fall and fail you; the terrible confounders, lurking in the dark; the ill-measured data that haunt your inferences! But once you...2020-07-301h 03Learning Bayesian Statistics#20 Regression and Other Stories, with Andrew Gelman, Jennifer Hill & Aki VehtariOnce upon a time, there was an enchanted book filled with hundreds of little plots, applied examples and linear regressions — the prettiest creature that was ever seen. Its authors were excessively fond of it, and its readers loved it even more. This magical book had a nice blue cover made for it, and everybody aptly called it « Regression and other Stories »!As every good fairy tale, this one had its share of villains — the traps where statistical methods fall and fail you; the terrible confounders, lurking in the dark; the ill-measured data that haunt your inferences! But once you...2020-07-301h 03
Learning Bayesian Statistics#SpecialAnnouncement: Patreon Launched!I hope you’re all safe! Some of you also asked me if I had set up a Patreon so that they could help support the show, and that’s why I’m sending this short special episode your way today. I had thought about that, but I wasn’t sure there was a demand for this. Apparently, there is one — at least a small one — so, first, I wanna thank you and say how grateful I am to be in a community that values this kind of work!The Patreon page is now live at patreon.com/learnbay...2020-06-2607 minLearning Bayesian Statistics#SpecialAnnouncement: Patreon Launched!I hope you’re all safe! Some of you also asked me if I had set up a Patreon so that they could help support the show, and that’s why I’m sending this short special episode your way today. I had thought about that, but I wasn’t sure there was a demand for this. Apparently, there is one — at least a small one — so, first, I wanna thank you and say how grateful I am to be in a community that values this kind of work!The Patreon page is now live at patreon.com/learnbay...2020-06-2607 min