Learning Bayesian Statistics
Alexandre Andorra
Are you a researcher or data scientist / analyst / ninja? Do you want to learn Bayesian inference, stay up to date or simply want to understand what Bayesian inference is? Then this podcast is for you! You'll hear from researchers and practitioners of all fields about how they use Bayesian statistics, and how in turn YOU can apply these methods in your modeling workflow. When I started learning Bayesian methods, I really wished there were a podcast out there that could introduce me to the methods, the projects and the people who make all that possible. So I created "Learning Bayesian Statistics", where you'll get to hear how Bayesian statistics are used to detect black matter in outer space, forecast elections or understand how diseases spread and can ultimately be stopped. But this show is not only about successes -- it's also about failures, because that's how we learn best. So you'll often hear the guests talking about what *didn't* work in their projects, why, and how they overcame these challenges. Because, in the end, we're all lifelong learners! My name is Alex Andorra by the way. By day, I'm a Senior data scientist. By night, I don't (yet) fight crime, but I'm an open-source enthusiast and core contributor to the python packages PyMC and ArviZ. I also love Nutella, but I don't like talking about it – I prefer eating it. So, whether you want to learn Bayesian statistics or hear about the latest libraries, books and applications, this podcast is for you -- just subscribe! You can also support the show and unlock exclusive Bayesian swag on Patreon!
Episodes
Mentioned books
Apr 8, 2026 • 1h 54min
#155 Probabilistic Programming for the Real World, with Andreas Munk
Andreas Munk, researcher and entrepreneur in probabilistic programming who co-founded Evara and helped build PyProb. He discusses bridging deep learning with probabilistic programming. He explains inference compilation and amortized inference. He describes probabilistic surrogate networks for costly simulators. He demos embedding Bayesian workflows into Excel for practical decision-making.
Apr 2, 2026 • 5min
Bitesize | "What Would Have Happened?" - Bayesian Synthetic Control Explained
Thomas Pinder, researcher in Bayesian causal inference, reframes synthetic control as a Bayesian regression problem. He explains using Dirichlet priors on weights, tuning concentration or placing hyperpriors, and why the Bayesian approach yields richer, less fragile counterfactuals for real-world decision making.
Mar 25, 2026 • 1h 26min
#154 Bayesian Causal Inference at Scale, with Thomas Pinder
Thomas Pinder, statistician and GPJax creator focused on Gaussian processes and Bayesian causal inference. He explains why GPJax was built for flexible, high-performance GP research. He discusses Bayesian approaches to causal inference, synthetic controls and SDID, and practical tradeoffs for scaling GPs and using hierarchical models in industry.
10 snips
Mar 11, 2026 • 1h 9min
#153 The Neuroscience of Philanthropy, with Cherian Koshy
Cherian Koshy, a fundraising and behavioral science expert who studies the neuroscience of generosity. He explains why generosity is hardwired, how beliefs and identity shape giving, and why small asks and low-friction donation flows grow donors. He also explores AI that strengthens donor memory, the generosity gap, and how science clarifies effective fundraising strategies.
Mar 4, 2026 • 4min
Bitesize | How To Model Risk Aversion In Pricing?
Daniel Saunders, an applied Bayesian researcher in pricing optimization, walks through how to model risk aversion in price-setting using exponential utility. He explains why uncertainty grows at high prices. He shows how adding a risk parameter shifts recommendations toward safer, lower-price choices with tighter profit distributions.
24 snips
Feb 26, 2026 • 1h 19min
#152 A Bayesian decision theory workflow, with Daniel Saunders
Daniel Saunders, a philosopher-turned-Bayesian practitioner who builds decision-theory workflows and PyMC/PyTensor tooling. He discusses separating beliefs from utilities for team workflows. He explains shifting evaluation from accuracy to business value and demonstrates vectorized posterior optimization with PyTensor for pricing and profit. He covers risk-averse utility transforms and practical safeguards for industrial decision making.
5 snips
Feb 19, 2026 • 4min
BITESIZE | How Do Diffusion Models Work?
Jonas Arruda, a researcher who explains diffusion models and generative modeling, gives a clear mini-tutorial. He walks through starting from Gaussian noise and iteratively denoising to produce samples. He contrasts the forward noising process with the learned backward denoising. He also outlines training with noisy parameters and the role of noise schedules like alpha and sigma.
Feb 12, 2026 • 1h 36min
#151 Diffusion Models in Python, a Live Demo with Jonas Arruda
Jonas Arruda, mathematician and PhD researcher at the University of Bonn and core contributor to Baseflow. He explores using diffusion models for simulation-based inference. Short demos show building simulators, training amortized posteriors, and sampling multimodal results. Discussion covers score learning, reverse sampling, flow-matching alternatives, diagnostics like coverage/SBC, and practical tips for scaling and guided inference.
18 snips
Jan 28, 2026 • 1h 20min
#150 Fast Bayesian Deep Learning, with David Rügamer, Emanuel Sommer & Jakob Robnik
David Rügamer, LMU professor working on uncertainty in deep models; Emanuel Sommer, PhD researcher building practical JAX sampling tools; Jakob Robnik, Berkeley physicist developing the Microcanonical Langevin sampler. They discuss scaling Bayesian neural networks, fast sampling tricks and software, microcanonical dynamics, bottlenecks in high dimensions, hybrid warm-start strategies, and tooling for practical uncertainty quantification.
Jan 21, 2026 • 25min
BITESIZE | Building Resilience in Modern Tech Careers
Join Alana Karen, a technology strategist with a keen insight into Silicon Valley's trends, as she discusses the shifting landscape of tech careers. Delve into the psychological impacts of mass layoffs and how they reshape employee creativity. Alana highlights the push for return-to-office policies and the power dynamics behind it. She also explores the rapid rise of AI, emphasizing the necessity of developing complex skills to stay relevant. Tune in for a forward-looking perspective on adapting to the future of work!
