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
May 28, 2025 • 1h 12min
#133 Making Models More Efficient & Flexible, with Sean Pinkney & Adrian Seyboldt
Sean Pinkney, a managing director at Omnicom Media Group and Stan contributor, teams up with Adrian Seyboldt, creator of NutBuy, to delve into innovative statistical modeling. They discuss enhancing hierarchical models with zero-sum constraints and the vital differences between population and sample means. Insights on Cholesky parameterization and improved sampling techniques are also explored. Their collaboration emphasizes how sharing knowledge fosters research advancements, making complex statistical problems more approachable and efficient.
May 21, 2025 • 22min
BITESIZE | How AI is Redefining Human Interactions, with Tom Griffiths
In this discussion, Professor Tom Griffiths from Princeton University, an expert in psychology and computer science, shares insights on the interplay between human and artificial intelligence. He highlights key differences in learning processes, emphasizing that AI should enhance human capabilities rather than merely mimic them. Tom addresses how AI can help overcome human biases, improve decision-making, and align better with human cognition. The conversation underscores the need for AI models that reflect human understanding to make more effective systems.
6 snips
May 13, 2025 • 1h 30min
#132 Bayesian Cognition and the Future of Human-AI Interaction, with Tom Griffiths
In this discussion, Tom Griffiths, a Henry Luce professor at Princeton, bridges psychology and computer science. He reveals how Bayesian statistics can enhance our understanding of human cognition and learning. The conversation touches on the importance of individual responses over averages, and how generative AI mirrors human cognitive processes. Griffiths explains the fundamental differences between human and machine intelligence, emphasizing the potential for AI to improve human decision-making while navigating challenges in language learning and alignment.
May 7, 2025 • 14min
BITESIZE | Hacking Bayesian Models for Better Performance, with Luke Bornn
Luke Bornn, a sports analytics expert specializing in generative models, dives into the fascinating world of Bayesian modeling. He discusses how to effectively integrate spatial and temporal data to predict outcomes in sports. The conversation touches on the challenges of creating interpretable priors and optimizing model performance. Luke also shares innovative methods for improving Bayesian models while navigating complexities in computation and posterior sampling. Tune in for insights that blend statistical prowess with sports strategy!
8 snips
Apr 30, 2025 • 1h 32min
#131 Decision-Making Under High Uncertainty, with Luke Bornn
Luke Bornn, a pioneer in sports analytics and co-founder of Zealous Analytics, shares insights from his rich background in statistics and sports. He dives into the evolution of player tracking data and the role of Bayesian methods in decision-making amid uncertainty. There are personal stories about mentorship in sports and statistics, along with lighthearted tales of family gaming. Bornn discusses the complexities of statistical models and their impact on player acquisition and injury prediction, emphasizing the need for interdisciplinary approaches.
Apr 23, 2025 • 16min
BITESIZE | Real-World Applications of Models in Public Health, with Adam Kucharski
In this engaging discussion, Adam Kucharski, an epidemiological modeler known for his work during the COVID-19 pandemic, delves into the pivotal role of patient modeling in shaping public health responses. He stresses the importance of effective communication regarding data interpretation to combat misconceptions. Kucharski also highlights the complexities of linking modeling outputs to policy decisions and advocates for enhanced probabilistic thinking in public discourse. Through scenario visualizations, he illustrates how models can help the public understand epidemics better.
Apr 16, 2025 • 1h 9min
#130 The Real-World Impact of Epidemiological Models, with Adam Kucharski
Adam Kucharski, a professor of infectious disease epidemiology, dives into the art of epidemic modeling and its vital role during crises like COVID-19. He discusses the challenges of communicating complex models to the public and the importance of Bayesian statistics in navigating uncertainty. The conversation also explores how ideas and diseases spread similarly, emphasizing the need for collaborative efforts in public health. Plus, Kucharski reflects on the impact of AI in improving data interpretation and decision-making in epidemiology.
Apr 9, 2025 • 12min
BITESIZE | The Why & How of Bayesian Deep Learning, with Vincent Fortuin
Vincent Fortuin, an AI expert and researcher, dives deep into Bayesian deep learning and how it stacks up against traditional models. He highlights the mathematical nuances and uncertainties in predictions that Bayesian methods bring to the table. The conversation also tackles the lack of cohesive libraries for Bayesian techniques and offers insights on tackling the complexities of real-world applications, particularly in healthcare and climate science. Tune in for fascinating details about improving model interpretability and enhancing AI usability!
7 snips
Apr 2, 2025 • 1h 3min
#129 Bayesian Deep Learning & AI for Science with Vincent Fortuin
Vincent Fortuin, a tenure-track research leader at Helmholtz AI, dives into the world of Bayesian deep learning and its transformative power in scientific research. He discusses the contradictions in AI hype versus practical outcomes and highlights how combining Bayesian statistics with deep learning enhances prediction reliability. Fortuin emphasizes the significance of prior knowledge and argues for better uncertainty communication in AI applications. He also touches on innovative techniques that improve data efficiency and acknowledges the need for collaboration in the community.
Mar 19, 2025 • 58min
#128 Building a Winning Data Team in Football, with Matt Penn
Matt Penn, a football data scientist for Como 1907, discusses the critical role of Bayesian statistics in player recruitment and team decisions. He highlights how analysts can effectively communicate insights to coaching staff, fostering better collaboration. The conversation dives into the biases present in traditional football statistics and the impact of tracking data on understanding player movements. Matt shares his journey into sports analytics and emphasizes the importance of curiosity and practical experience for aspiring analysts in this competitive landscape.
