#Bayesian

Many people struggle to understand #Bayesian updating! So I made this super cool #Python @matplotlib #educational, interactive dashboard. Given a positive test, '+', what is the probability that the an 'Event' is happening? Change the probability of the event, P(Event), true positive, P(+|Event), and false positive, P(-|Event), conditional probabilities, then watch the change in the posterior, P(Event|+)! I also added P(Not Event|+), P(Event|-) & P(Not Event|-). Try it out on ∀.

Yesterday, I taught Bayesian Linear Regression in my #MachineLearning course. I began by introducing the fundamentals of Bayesian probability, then framed linear regression within a #Bayesian context. Afterward, I delved into Markov Chain Monte Carlo (MCMC) methods, demonstrating how they can be used to sample from the posterior distributions of the model parameters. Follow along on #YouTube @ and with my free, online e-book @ ∀.

I really love teaching Bayesian linear regression. It’s the perfect introduction to #Bayesian methods for #MachineLearning. Spoiler alert: they’re awesome. 🔥 In class, I walk students step-by-step through: 1️⃣ How the prior is updated with the likelihood to produce the posterior 2️⃣ How we sample that posterior using Markov Chain #MonteCarlo (MCMC) — specifically Metropolis sampling Then we open up my interactive #Python dashboard and actually do it: Sample the Markov chain of model parameters, compute the acceptance probability directly from prior × likelihood ∝ posterior Theory → algorithm → live visualization. No black boxes. Just probability in motion. 🚀 That moment when students see the posterior emerge from the sampling process? Completely stoked. 🤘 I share the full interactive notebook here: #GitHub

Applied #Bayesian Modeling starts Nov 2: four live weeks of Bayesian modeling with #PyMC, taught by the people who created PyMC and Stan and wrote Think Bayes. Instructor Evan Wimpey explains why the goal is a model you can understand and trust. Enroll:

Bayesian statistics for clinical research This review provides a brief account of the philosophical and methodological differences between Bayesian and frequentist approaches and survey the use of Bayesian methods for the design and analysis of clinical research. #Bayesian #bayestheorem #ClinicalTrial #Probabilities

Loved this post about Bayesian inference and Hamiltonian Monte Carlo by @rlmcelreath 🔗 And the wonderful MCMC interactive gallery by Chi Feng! 🔗 #bayesian #statistics #mcmc @mcmc_stan Can your Markov chain do this? 👇

#Bayesian updating is incredible! It's both powerful and intuitive. To help my students fully grasp and visualize this concept, I built an interactive #Python dashboard using @matplotlib. Students can adjust key variables, such as the prior (probability of the event), likelihood (probability of a positive test given the event is happening), and false positive rate, and see how these influence the posterior probability. The dashboard calculates: Probability of the event happening given a positive test and, Probability of the event happening given a negative test Probability of the event not happening given a positive test Probability of the event not happening given a negative test I designed custom plots from scratch to enhance the learning experience, and the dashboard is available on my #GitHub: ∀. #DataScience #InteractiveLearning

Many students have trouble understanding #Bayesian #MachineLearning, to help them I coded up Bayesian linear regression with MCMC to sample the posterior from scratch. Then I built out this interactive #Python dashboard with @matplotlib. Vary the number of MCMC samples and watch the transition from burn-in to equilibrium chain. Try it out on #GitHub @ ∀.

#Bayesian updating is truly fascinating! It reveals a wealth of interesting behaviors and insights! For example, given a prior (green) and a likelihood (red), as we reduce the variance of the likelihood, the posterior (black) converges to the likelihood. The prior becomes less influential as the likelihood becomes increasingly informative! Try out my set of interactive #Python Bayesian updating demonstrations on #GitHub @ ∀. #DataScience

got two new #bayesian books in the ✉️ today 👀 can’t wait to design a new course based on them @cocosci_lab @weijima01 @KordingLab @dangoldreich

If learning #Bayesian modeling was on your list for 2026, this is your last chance this year. Applied Bayesian Modeling with #PyMC, our final cohort of 2026, starts Monday, Nov 2: eight live sessions. Reserve your seat:

When I teach #Bayesian #Statistics in class, I hold up a coin and ask the students, 'Is this a fair coin?'. Then I go through Bayesian updating and we formulate prior and likelihood together to update to a posterior! We use my interactive #Python @matplotlib Sivia's coin toss demo, on #GitHub @ ∀. #DataScience

ICYMI, 👍 explorable: “Bayesian inference - an interactive visualization” by @krstoffr #visualization #bayesian #d3js

I'm teaching #Bayesian methods in my #DataAnalytics course this week. To support my students, I built this custom #Python dashboard with @matplotlib. Change the prior, likelihood and observed the posterior on #GitHub @ ∀. #DataScience #Statistics

Full graduate course in #Bayesian statistics (videos + slides + homework). #DataScience #MachineLearning

Folk from the #Bayesian World, rejoice! 🚀 This survey/tutorial will navigate you through the #DeepLearning aspects with a wide range of Conception, Learning Algorithms, Evaluations, and Toolsets 💯🙌

Given a positive test result, what is the #probability of the event happening? or not happening? or if a negative test result? A great example to learn #Bayesian updating - prior updated with new data to calculate a posterior! I built out an interactive #Python @matplotlib #dashboard on #GitHub @ ∀. #DataScience

Building slides for my course on Intuitive #Bayesian statistics. Learn more and sign up:

New post: Hierarchical Bayesian Neural Networks with Informative Priors #deeplearning #bayesian #pydata #pymc3

Prof Larry Wasserman’s (CMU) talk “problems with Bayesian causal inference” #bayesian #causality