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All Episodes

Learning Bayesian Statistics — 21 episodes

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Title
1

Bayesian Principal Stratification: Modeling Treatment Effects

2

Why a Bayesian Workflow Goes Beyond Fitting Models

3

#164 Bayesian Workflow, with Andrew Gelman, Aki Vehtari & Richard McElreath

4

Making Gaussian Processes Easier to Use

5

The Future of Faster MCMC

6

#163 How to make your models sample faster, with Adrian Seyboldt & Eliot Carlson

7

Bayesian Statistics vs. Epistemology

8

Bayesian Epistemology Is "Bayes' Theorem Without the Data"

9

Why Bayesians Have an Edge in AI

10

#162 Bayesian Hydrology & GPU AI, with Christopher Krapu

11

The Next Step Beyond LLMs: Foundation Models for Inference

12

#161 Amortized Inference & Neural Processes, with Luigi Acerbi

13

Bayesian Statistics vs Epistemology, with Vaden Masrani

14

Why Bayesian Statistics Is More Computational Than Ever

15

Exact GPs vs Approximations: When to Use Each (and Why It Matters)

16

#159 Bayesian Occupancy Models, with Matthijs Hollanders

17

Can AI Learn What Experts Know? Automating Prior Elicitation with Generative Models

18

#158 Bayesian Workflows & Foundation Models, with Stefan Radev

19

The Hidden Geometry of Hierarchical Models

20

#157 Amortized Inference & BayesFlow in Practice, with Stefan Radev

21

How to Design Better Experiments with Expected Information Gain