Pierre-Simon Laplace
@learnbayesstats.bsky.social
A podcast on #BayesianStats -- the methods, the projects, the people
By @alex-andorra.bsky.social
Listen: http://tinyurl.com/pvz4ekky
Support: http://tinyurl.com/2p8mpxnp
By @alex-andorra.bsky.social
Listen: http://tinyurl.com/pvz4ekky
Support: http://tinyurl.com/2p8mpxnp
Pinned
Clinical trials don’t fail because patients fail.
They fail when designs stop learning.
Episode 148 of Learning Bayesian Statistics explores adaptive & platform trials and why "wait for the final analysis" isn’t neutral in ALS or pandemics.
🔗 learnbayesstats.com/episode/148-...
#newEpisode #bayes
They fail when designs stop learning.
Episode 148 of Learning Bayesian Statistics explores adaptive & platform trials and why "wait for the final analysis" isn’t neutral in ALS or pandemics.
🔗 learnbayesstats.com/episode/148-...
#newEpisode #bayes
Reposted by Pierre-Simon Laplace
My #AdvancedRegressionModeling course, written with the brilliant Ravin Kumar and @tomicapretto.bsky.social, is now available through my Topmate profile!
So do give it a try and let me know what you think in the comments 👇
See you soon in the Intuitive Bayes' Discourse 🖖
topmate.io/alex_andorra...
So do give it a try and let me know what you think in the comments 👇
See you soon in the Intuitive Bayes' Discourse 🖖
topmate.io/alex_andorra...
Your All-in-One Creator Storefront
Make money from your content. Sell products, host sessions, and grow your business — all from a single link.
topmate.io
January 2, 2026 at 6:23 PM
My #AdvancedRegressionModeling course, written with the brilliant Ravin Kumar and @tomicapretto.bsky.social, is now available through my Topmate profile!
So do give it a try and let me know what you think in the comments 👇
See you soon in the Intuitive Bayes' Discourse 🖖
topmate.io/alex_andorra...
So do give it a try and let me know what you think in the comments 👇
See you soon in the Intuitive Bayes' Discourse 🖖
topmate.io/alex_andorra...
Clinical trials don’t fail because patients fail.
They fail when designs stop learning.
Episode 148 of Learning Bayesian Statistics explores adaptive & platform trials and why "wait for the final analysis" isn’t neutral in ALS or pandemics.
🔗 learnbayesstats.com/episode/148-...
#newEpisode #bayes
They fail when designs stop learning.
Episode 148 of Learning Bayesian Statistics explores adaptive & platform trials and why "wait for the final analysis" isn’t neutral in ALS or pandemics.
🔗 learnbayesstats.com/episode/148-...
#newEpisode #bayes
January 1, 2026 at 4:27 PM
Clinical trials don’t fail because patients fail.
They fail when designs stop learning.
Episode 148 of Learning Bayesian Statistics explores adaptive & platform trials and why "wait for the final analysis" isn’t neutral in ALS or pandemics.
🔗 learnbayesstats.com/episode/148-...
#newEpisode #bayes
They fail when designs stop learning.
Episode 148 of Learning Bayesian Statistics explores adaptive & platform trials and why "wait for the final analysis" isn’t neutral in ALS or pandemics.
🔗 learnbayesstats.com/episode/148-...
#newEpisode #bayes
Fast Bayesian inference is great… until you’re babysitting convergence.
@alex-andorra.bsky.social is joined by Martin Ingram to explore DADVI a more predictable, less noisy approach to variational inference that makes trade-offs explicit instead of mysterious
🎧 lnkd.in/gAX2iaHz
#bayesianinference
@alex-andorra.bsky.social is joined by Martin Ingram to explore DADVI a more predictable, less noisy approach to variational inference that makes trade-offs explicit instead of mysterious
🎧 lnkd.in/gAX2iaHz
#bayesianinference
December 15, 2025 at 6:17 PM
Fast Bayesian inference is great… until you’re babysitting convergence.
@alex-andorra.bsky.social is joined by Martin Ingram to explore DADVI a more predictable, less noisy approach to variational inference that makes trade-offs explicit instead of mysterious
🎧 lnkd.in/gAX2iaHz
#bayesianinference
@alex-andorra.bsky.social is joined by Martin Ingram to explore DADVI a more predictable, less noisy approach to variational inference that makes trade-offs explicit instead of mysterious
🎧 lnkd.in/gAX2iaHz
#bayesianinference
🎙️ How do you tackle extreme physics experiments? Ethan Smith shares insights with @alex-andorra.bsky.social
✅ Bayesian inference for sparse, noisy data
✅ Priors guide well-established physical models
✅ Scaling Bayesian workflows across teams
🎧 lnkd.in/geA2kQm6
#Bayesian #LearningBayesianStats
✅ Bayesian inference for sparse, noisy data
✅ Priors guide well-established physical models
✅ Scaling Bayesian workflows across teams
🎧 lnkd.in/geA2kQm6
#Bayesian #LearningBayesianStats
December 2, 2025 at 4:49 PM
🎙️ How do you tackle extreme physics experiments? Ethan Smith shares insights with @alex-andorra.bsky.social
✅ Bayesian inference for sparse, noisy data
✅ Priors guide well-established physical models
✅ Scaling Bayesian workflows across teams
🎧 lnkd.in/geA2kQm6
#Bayesian #LearningBayesianStats
✅ Bayesian inference for sparse, noisy data
✅ Priors guide well-established physical models
✅ Scaling Bayesian workflows across teams
🎧 lnkd.in/geA2kQm6
#Bayesian #LearningBayesianStats
🎙️ What does it take to grow in tech? Jordan Thibodeau shares lessons from years inside top tech cultures with @alex-andorra.bsky.social
✅ Bayesian thinking as a practical advantage
✅ AI amplifies skill, not replaces it
✅ Networking & sharing knowledge matter
🎧 lnkd.in/ghk6D6nH
#bayes #career
✅ Bayesian thinking as a practical advantage
✅ AI amplifies skill, not replaces it
✅ Networking & sharing knowledge matter
🎧 lnkd.in/ghk6D6nH
#bayes #career
November 14, 2025 at 2:24 PM
🎙️ What does it take to grow in tech? Jordan Thibodeau shares lessons from years inside top tech cultures with @alex-andorra.bsky.social
✅ Bayesian thinking as a practical advantage
✅ AI amplifies skill, not replaces it
✅ Networking & sharing knowledge matter
🎧 lnkd.in/ghk6D6nH
#bayes #career
✅ Bayesian thinking as a practical advantage
✅ AI amplifies skill, not replaces it
✅ Networking & sharing knowledge matter
🎧 lnkd.in/ghk6D6nH
#bayes #career
Reposted by Pierre-Simon Laplace
Now I'm also looking for a research software engineer to implement a pile of research results to R packages loo, posterior, bayesplot, projpred, priorsense, brms or/and Python packages ArviZ, Bambi and Kulprit. Apply by email with no specific deadline (see contact info at users.aalto.fi/~ave/)
I'm now also looking for a postdoc with strong Bayesian background and interest in developing Bayesian cross-validation theory, methods and software. Apply by email with no specific deadline (see contact information at users.aalto.fi/~ave/).
Others, please share
Others, please share
I'm looking for a doctoral student with Bayesian background to work on Bayesian workflow and cross-validation (see my publication list users.aalto.fi/~ave/publica... for my recent work) at Aalto University.
Apply through the ELLIS PhD program (dl October 31) ellis.eu/news/ellis-p...
Apply through the ELLIS PhD program (dl October 31) ellis.eu/news/ellis-p...
November 3, 2025 at 11:13 AM
Now I'm also looking for a research software engineer to implement a pile of research results to R packages loo, posterior, bayesplot, projpred, priorsense, brms or/and Python packages ArviZ, Bambi and Kulprit. Apply by email with no specific deadline (see contact info at users.aalto.fi/~ave/)
Bayesian deep learning helps ML models understand their uncertainty
In this episode @alex-andorra.bsky.social talks with Maurizio Filippone about Gaussian Processes, scalable inference, MCMC, and Bayesian deep learning at scale
🎧 learnbayesstats.com/episode/144-...
#BayesianStats #AI #ML #Bayes
In this episode @alex-andorra.bsky.social talks with Maurizio Filippone about Gaussian Processes, scalable inference, MCMC, and Bayesian deep learning at scale
🎧 learnbayesstats.com/episode/144-...
#BayesianStats #AI #ML #Bayes
November 1, 2025 at 4:14 PM
Bayesian deep learning helps ML models understand their uncertainty
In this episode @alex-andorra.bsky.social talks with Maurizio Filippone about Gaussian Processes, scalable inference, MCMC, and Bayesian deep learning at scale
🎧 learnbayesstats.com/episode/144-...
#BayesianStats #AI #ML #Bayes
In this episode @alex-andorra.bsky.social talks with Maurizio Filippone about Gaussian Processes, scalable inference, MCMC, and Bayesian deep learning at scale
🎧 learnbayesstats.com/episode/144-...
#BayesianStats #AI #ML #Bayes
🍽️ Can better nutrition science come from better statistics?
In the latest episode, @alex-andorra.bsky.social chats with Christoph Bamberg about using a Bayesian mindset to make psychology & nutrition research more transparent and actionable
🎧 learnbayesstats.com/episode/143-...
#bayes #nutrition
In the latest episode, @alex-andorra.bsky.social chats with Christoph Bamberg about using a Bayesian mindset to make psychology & nutrition research more transparent and actionable
🎧 learnbayesstats.com/episode/143-...
#bayes #nutrition
October 17, 2025 at 4:09 PM
🍽️ Can better nutrition science come from better statistics?
In the latest episode, @alex-andorra.bsky.social chats with Christoph Bamberg about using a Bayesian mindset to make psychology & nutrition research more transparent and actionable
🎧 learnbayesstats.com/episode/143-...
#bayes #nutrition
In the latest episode, @alex-andorra.bsky.social chats with Christoph Bamberg about using a Bayesian mindset to make psychology & nutrition research more transparent and actionable
🎧 learnbayesstats.com/episode/143-...
#bayes #nutrition
Reposted by Pierre-Simon Laplace
How to run #BART and #TreeModels fast in #Python -- new episode is out, with @gstechschulte.bsky.social !
🤔How do you keep Bayesian rigor when the data’s too big to behave?
@gstechschulte.bsky.social joins @alex-andorra.bsky.social on Learning Bayesian Statistics to talk BART and how they’re bridging classic stats with modern, large-scale systems.
🎧 Listen here: learnbayesstats.com/episode/142-...
@gstechschulte.bsky.social joins @alex-andorra.bsky.social on Learning Bayesian Statistics to talk BART and how they’re bridging classic stats with modern, large-scale systems.
🎧 Listen here: learnbayesstats.com/episode/142-...
October 6, 2025 at 5:48 PM
How to run #BART and #TreeModels fast in #Python -- new episode is out, with @gstechschulte.bsky.social !
🤔How do you keep Bayesian rigor when the data’s too big to behave?
@gstechschulte.bsky.social joins @alex-andorra.bsky.social on Learning Bayesian Statistics to talk BART and how they’re bridging classic stats with modern, large-scale systems.
🎧 Listen here: learnbayesstats.com/episode/142-...
@gstechschulte.bsky.social joins @alex-andorra.bsky.social on Learning Bayesian Statistics to talk BART and how they’re bridging classic stats with modern, large-scale systems.
🎧 Listen here: learnbayesstats.com/episode/142-...
October 6, 2025 at 4:27 PM
🤔How do you keep Bayesian rigor when the data’s too big to behave?
@gstechschulte.bsky.social joins @alex-andorra.bsky.social on Learning Bayesian Statistics to talk BART and how they’re bridging classic stats with modern, large-scale systems.
🎧 Listen here: learnbayesstats.com/episode/142-...
@gstechschulte.bsky.social joins @alex-andorra.bsky.social on Learning Bayesian Statistics to talk BART and how they’re bridging classic stats with modern, large-scale systems.
🎧 Listen here: learnbayesstats.com/episode/142-...
🧪 Causal inference is about understanding why things happen, not just what
@alex-andorra.bsky.social talks with Sam Witty about ChiRho & how probabilistic programming is reshaping interventions, counterfactuals, and the future of causal reasoning
🎧 learnbayesstats.com/episode/141-...
#newepisode
@alex-andorra.bsky.social talks with Sam Witty about ChiRho & how probabilistic programming is reshaping interventions, counterfactuals, and the future of causal reasoning
🎧 learnbayesstats.com/episode/141-...
#newepisode
September 20, 2025 at 3:53 PM
🧪 Causal inference is about understanding why things happen, not just what
@alex-andorra.bsky.social talks with Sam Witty about ChiRho & how probabilistic programming is reshaping interventions, counterfactuals, and the future of causal reasoning
🎧 learnbayesstats.com/episode/141-...
#newepisode
@alex-andorra.bsky.social talks with Sam Witty about ChiRho & how probabilistic programming is reshaping interventions, counterfactuals, and the future of causal reasoning
🎧 learnbayesstats.com/episode/141-...
#newepisode
🏈 NFL meets Bayesian stats!
In this episode @alex-andorra.bsky.social chats with Ron Yurko on
👉 Writing your own models
👉 Building a sports analytics portfolio
👉 Pitfalls of modelling expectations
👉 Using tracking data for player insights
👉 Causal thinking in football data
🎧 lnkd.in/gWz4v2JG
In this episode @alex-andorra.bsky.social chats with Ron Yurko on
👉 Writing your own models
👉 Building a sports analytics portfolio
👉 Pitfalls of modelling expectations
👉 Using tracking data for player insights
👉 Causal thinking in football data
🎧 lnkd.in/gWz4v2JG
September 9, 2025 at 5:42 PM
🏈 NFL meets Bayesian stats!
In this episode @alex-andorra.bsky.social chats with Ron Yurko on
👉 Writing your own models
👉 Building a sports analytics portfolio
👉 Pitfalls of modelling expectations
👉 Using tracking data for player insights
👉 Causal thinking in football data
🎧 lnkd.in/gWz4v2JG
In this episode @alex-andorra.bsky.social chats with Ron Yurko on
👉 Writing your own models
👉 Building a sports analytics portfolio
👉 Pitfalls of modelling expectations
👉 Using tracking data for player insights
👉 Causal thinking in football data
🎧 lnkd.in/gWz4v2JG
What if your optimization algorithm could explain its uncertainty as clearly as its results?” 🤔
In this episode🎙️ @alex-andorra.bsky.social dives into Bayesian optimization, BoTorch, and why uncertainty matters with Maximilian Balandat
🎧 Listen here: lnkd.in/gg6fcfFU
#bayesian #pytorch #podcast
In this episode🎙️ @alex-andorra.bsky.social dives into Bayesian optimization, BoTorch, and why uncertainty matters with Maximilian Balandat
🎧 Listen here: lnkd.in/gg6fcfFU
#bayesian #pytorch #podcast
August 22, 2025 at 4:02 PM
What if your optimization algorithm could explain its uncertainty as clearly as its results?” 🤔
In this episode🎙️ @alex-andorra.bsky.social dives into Bayesian optimization, BoTorch, and why uncertainty matters with Maximilian Balandat
🎧 Listen here: lnkd.in/gg6fcfFU
#bayesian #pytorch #podcast
In this episode🎙️ @alex-andorra.bsky.social dives into Bayesian optimization, BoTorch, and why uncertainty matters with Maximilian Balandat
🎧 Listen here: lnkd.in/gg6fcfFU
#bayesian #pytorch #podcast
Your deep learning model might be confidently wrong — and in medicine or epidemiology, that’s dangerous.
In this episode, @alex-andorra.bsky.social chats with Mélodie Monod, François-Xavier & Yingzhen Li about making neural nets more reliable, Bayesian LLMs & more
🎧 lnkd.in/gcaRQXcb
#bayes #llm
In this episode, @alex-andorra.bsky.social chats with Mélodie Monod, François-Xavier & Yingzhen Li about making neural nets more reliable, Bayesian LLMs & more
🎧 lnkd.in/gcaRQXcb
#bayes #llm
August 8, 2025 at 3:56 PM
Your deep learning model might be confidently wrong — and in medicine or epidemiology, that’s dangerous.
In this episode, @alex-andorra.bsky.social chats with Mélodie Monod, François-Xavier & Yingzhen Li about making neural nets more reliable, Bayesian LLMs & more
🎧 lnkd.in/gcaRQXcb
#bayes #llm
In this episode, @alex-andorra.bsky.social chats with Mélodie Monod, François-Xavier & Yingzhen Li about making neural nets more reliable, Bayesian LLMs & more
🎧 lnkd.in/gcaRQXcb
#bayes #llm
Models need more than pattern-matching.
They need causal understanding.
In this episode, Robert Ness joins @alex-andorra.bsky.social to explore:
⚡ Why models need real-world biases
🧠 How causal rep learning is reshaping AI
🤖 What it takes to add causality to DL
🎧 lnkd.in/gUnCkwEP
#bayes #podcast
They need causal understanding.
In this episode, Robert Ness joins @alex-andorra.bsky.social to explore:
⚡ Why models need real-world biases
🧠 How causal rep learning is reshaping AI
🤖 What it takes to add causality to DL
🎧 lnkd.in/gUnCkwEP
#bayes #podcast
July 25, 2025 at 4:45 PM
Models need more than pattern-matching.
They need causal understanding.
In this episode, Robert Ness joins @alex-andorra.bsky.social to explore:
⚡ Why models need real-world biases
🧠 How causal rep learning is reshaping AI
🤖 What it takes to add causality to DL
🎧 lnkd.in/gUnCkwEP
#bayes #podcast
They need causal understanding.
In this episode, Robert Ness joins @alex-andorra.bsky.social to explore:
⚡ Why models need real-world biases
🧠 How causal rep learning is reshaping AI
🤖 What it takes to add causality to DL
🎧 lnkd.in/gUnCkwEP
#bayes #podcast
🚨 MCMC or INLA?
🤯 MCMC = slow sampling.
⚡ INLA = fast, smart approximations. No chains, no waiting.
🎙️ On LBS, @alex-andorra.bsky.social talks with Haavard Rue & Janet Van Niekerk about how INLA works, when to use it, and why it’s a game-changer.
🎧 Listen: lnkd.in/gp8D-RuU
#Bayesian #MCMC
🤯 MCMC = slow sampling.
⚡ INLA = fast, smart approximations. No chains, no waiting.
🎙️ On LBS, @alex-andorra.bsky.social talks with Haavard Rue & Janet Van Niekerk about how INLA works, when to use it, and why it’s a game-changer.
🎧 Listen: lnkd.in/gp8D-RuU
#Bayesian #MCMC
July 16, 2025 at 5:23 PM
🚨 MCMC or INLA?
🤯 MCMC = slow sampling.
⚡ INLA = fast, smart approximations. No chains, no waiting.
🎙️ On LBS, @alex-andorra.bsky.social talks with Haavard Rue & Janet Van Niekerk about how INLA works, when to use it, and why it’s a game-changer.
🎧 Listen: lnkd.in/gp8D-RuU
#Bayesian #MCMC
🤯 MCMC = slow sampling.
⚡ INLA = fast, smart approximations. No chains, no waiting.
🎙️ On LBS, @alex-andorra.bsky.social talks with Haavard Rue & Janet Van Niekerk about how INLA works, when to use it, and why it’s a game-changer.
🎧 Listen: lnkd.in/gp8D-RuU
#Bayesian #MCMC
🚨 Tired of MCMC cooking your CPU for hours?
@alex-andorra.bsky.social chats with Haavard Rue & Janet van Niekerk about INLA, a fast, deterministic game-changer for inference at scale.
✅ Handles huge + complex models
✅ Works with non-Gaussian likelihoods
🎧 www.learnbayesstats.com/episode/136-...
@alex-andorra.bsky.social chats with Haavard Rue & Janet van Niekerk about INLA, a fast, deterministic game-changer for inference at scale.
✅ Handles huge + complex models
✅ Works with non-Gaussian likelihoods
🎧 www.learnbayesstats.com/episode/136-...
July 10, 2025 at 4:24 PM
🚨 Tired of MCMC cooking your CPU for hours?
@alex-andorra.bsky.social chats with Haavard Rue & Janet van Niekerk about INLA, a fast, deterministic game-changer for inference at scale.
✅ Handles huge + complex models
✅ Works with non-Gaussian likelihoods
🎧 www.learnbayesstats.com/episode/136-...
@alex-andorra.bsky.social chats with Haavard Rue & Janet van Niekerk about INLA, a fast, deterministic game-changer for inference at scale.
✅ Handles huge + complex models
✅ Works with non-Gaussian likelihoods
🎧 www.learnbayesstats.com/episode/136-...
🧲 Got 50 predictors, but only 5 that matter?
Try the Horseshoe Prior — a Bayesian approach to sparse regression that shrinks noise, not signal.
Built with Bambi + @pymc.io
🔗 Full demo: bambinos.github.io/bambi/notebo...
#BayesianStatistics #Regression #HorseshoePrior #MarketingAnalytics #PyMC
Try the Horseshoe Prior — a Bayesian approach to sparse regression that shrinks noise, not signal.
Built with Bambi + @pymc.io
🔗 Full demo: bambinos.github.io/bambi/notebo...
#BayesianStatistics #Regression #HorseshoePrior #MarketingAnalytics #PyMC
July 8, 2025 at 4:43 PM
🧲 Got 50 predictors, but only 5 that matter?
Try the Horseshoe Prior — a Bayesian approach to sparse regression that shrinks noise, not signal.
Built with Bambi + @pymc.io
🔗 Full demo: bambinos.github.io/bambi/notebo...
#BayesianStatistics #Regression #HorseshoePrior #MarketingAnalytics #PyMC
Try the Horseshoe Prior — a Bayesian approach to sparse regression that shrinks noise, not signal.
Built with Bambi + @pymc.io
🔗 Full demo: bambinos.github.io/bambi/notebo...
#BayesianStatistics #Regression #HorseshoePrior #MarketingAnalytics #PyMC
Reposted by Pierre-Simon Laplace
New episode is out! A very practical one, where we dive into *how* to make sure your models *actually* answer the questions you're asking...
🔍 Most Bayesian models aren’t properly checked
Even when they converge, they might be wrong in ways you won’t see—unless you look differently
In this episode, Teemu Säilynoja joins @alex-andorra.bsky.social to explore, SBC, prior predictive checks and more!
🎧 learnbayesstats.com/episode/135-...
Even when they converge, they might be wrong in ways you won’t see—unless you look differently
In this episode, Teemu Säilynoja joins @alex-andorra.bsky.social to explore, SBC, prior predictive checks and more!
🎧 learnbayesstats.com/episode/135-...
June 28, 2025 at 10:50 PM
New episode is out! A very practical one, where we dive into *how* to make sure your models *actually* answer the questions you're asking...
🔍 Most Bayesian models aren’t properly checked
Even when they converge, they might be wrong in ways you won’t see—unless you look differently
In this episode, Teemu Säilynoja joins @alex-andorra.bsky.social to explore, SBC, prior predictive checks and more!
🎧 learnbayesstats.com/episode/135-...
Even when they converge, they might be wrong in ways you won’t see—unless you look differently
In this episode, Teemu Säilynoja joins @alex-andorra.bsky.social to explore, SBC, prior predictive checks and more!
🎧 learnbayesstats.com/episode/135-...
June 28, 2025 at 4:11 PM
🔍 Most Bayesian models aren’t properly checked
Even when they converge, they might be wrong in ways you won’t see—unless you look differently
In this episode, Teemu Säilynoja joins @alex-andorra.bsky.social to explore, SBC, prior predictive checks and more!
🎧 learnbayesstats.com/episode/135-...
Even when they converge, they might be wrong in ways you won’t see—unless you look differently
In this episode, Teemu Säilynoja joins @alex-andorra.bsky.social to explore, SBC, prior predictive checks and more!
🎧 learnbayesstats.com/episode/135-...
Your model says 97% confidence
But should you trust it?
Uncertainty in ML is still a hard problem
We’re hosting a meetup at Imperial College London on June 24 to dig into it — with our host @alex-andorra.bsky.social and other researchers working on better ways forward
🔗 lnkd.in/eainEJ9p
But should you trust it?
Uncertainty in ML is still a hard problem
We’re hosting a meetup at Imperial College London on June 24 to dig into it — with our host @alex-andorra.bsky.social and other researchers working on better ways forward
🔗 lnkd.in/eainEJ9p
June 18, 2025 at 5:54 PM
Your model says 97% confidence
But should you trust it?
Uncertainty in ML is still a hard problem
We’re hosting a meetup at Imperial College London on June 24 to dig into it — with our host @alex-andorra.bsky.social and other researchers working on better ways forward
🔗 lnkd.in/eainEJ9p
But should you trust it?
Uncertainty in ML is still a hard problem
We’re hosting a meetup at Imperial College London on June 24 to dig into it — with our host @alex-andorra.bsky.social and other researchers working on better ways forward
🔗 lnkd.in/eainEJ9p
Reposted by Pierre-Simon Laplace
New episode is out! In this one we nerd out quite deep on zero-sum constraints, and how to make your model sample faster 💨
🎙️ Ep. 133 is out now!
@alex-andorra.bsky.social chats with @spinkney.bsky.social
& Adrian Seyboldt about making Bayesian models more efficient without losing rigor — zero-sum constraints, Cholesky tricks, practical wins & more
🎧 learnbayesstats.com/episode/133-...
#Bayesianstats #podcast #LBS
@alex-andorra.bsky.social chats with @spinkney.bsky.social
& Adrian Seyboldt about making Bayesian models more efficient without losing rigor — zero-sum constraints, Cholesky tricks, practical wins & more
🎧 learnbayesstats.com/episode/133-...
#Bayesianstats #podcast #LBS
Learning Bayesian Statistics – Laplace to be for new & veteran Bayesians alike!
Laplace to be for new & veteran Bayesians alike!
learnbayesstats.com
June 2, 2025 at 3:48 PM
New episode is out! In this one we nerd out quite deep on zero-sum constraints, and how to make your model sample faster 💨
Thanks again for the guest star appearance @aseyboldt.bsky.social !! You're welcome back anytime ;)
June 13, 2025 at 4:21 PM
Thanks again for the guest star appearance @aseyboldt.bsky.social !! You're welcome back anytime ;)
Some people think R² doesn’t belong in Bayesian models
👇 David Kohns disagrees, and he has the math to back it
🎙️Ep. 134: @alex-andorra.bsky.social sits down with economist David Kohns to explore how modern Bayesian methods are reshaping time series modelling
🎧 learnbayesstats.com/episode/134-...
👇 David Kohns disagrees, and he has the math to back it
🎙️Ep. 134: @alex-andorra.bsky.social sits down with economist David Kohns to explore how modern Bayesian methods are reshaping time series modelling
🎧 learnbayesstats.com/episode/134-...
June 13, 2025 at 3:27 PM
Some people think R² doesn’t belong in Bayesian models
👇 David Kohns disagrees, and he has the math to back it
🎙️Ep. 134: @alex-andorra.bsky.social sits down with economist David Kohns to explore how modern Bayesian methods are reshaping time series modelling
🎧 learnbayesstats.com/episode/134-...
👇 David Kohns disagrees, and he has the math to back it
🎙️Ep. 134: @alex-andorra.bsky.social sits down with economist David Kohns to explore how modern Bayesian methods are reshaping time series modelling
🎧 learnbayesstats.com/episode/134-...
Reposted by Pierre-Simon Laplace
Ask me any questions you may have about this! #stats
🎙️ Ep. 133 is out now!
@alex-andorra.bsky.social chats with @spinkney.bsky.social
& Adrian Seyboldt about making Bayesian models more efficient without losing rigor — zero-sum constraints, Cholesky tricks, practical wins & more
🎧 learnbayesstats.com/episode/133-...
#Bayesianstats #podcast #LBS
@alex-andorra.bsky.social chats with @spinkney.bsky.social
& Adrian Seyboldt about making Bayesian models more efficient without losing rigor — zero-sum constraints, Cholesky tricks, practical wins & more
🎧 learnbayesstats.com/episode/133-...
#Bayesianstats #podcast #LBS
Learning Bayesian Statistics – Laplace to be for new & veteran Bayesians alike!
Laplace to be for new & veteran Bayesians alike!
learnbayesstats.com
June 2, 2025 at 1:23 PM
Ask me any questions you may have about this! #stats