Tag - Bayesian

A Bayesian election prediction, implemented with R and Stan

If the media coverage is anything to go by, people are desperate to know who will win the US election on November 8. Polls give us some indication of what's likely to happen, but any single poll isn't a great guide (despite the hype that accompanies...

A Bayesian Information Criterion for Singular Models

(This article was first published on R – JAGS News, and kindly contributed to R-bloggers) On Wednesday, Mathias Drton and I will be presenting a read paper on Bayesian model choice for singular models at the Royal Statistical Society in London. You c...

a typo that went under the radar

53 SHARES Share Tweet A chance occurrence on X validated: a question on an incomprehensible formula for Bayesian model choice: which, most unfortunately!, appeared in Bayesian Essentials with R! Eeech! It looks like one line in our LATEX file got era...

Afresh: Machine Learning Engineer

Seeking a Machine Learning Engineer to join founding-team for developing predictive models for the food supply chain, including forecasting demand, prices, inventory, etc. using deep learning combined with Bayesian statistics, causal inference, and o...

Bayesian Basics, Explained

This interview between Professor Andrew Gelman of Columbia University and marketing scientist Kevin Gray covers the basics of Bayesian statistics. Editor's note: The following is an interview with Columbia University Professor Andrew Gelman conducted...

Bayesian Inference with Backfitting MCMC

Share Tweet Previous posts in this series on MCMC samplers for Bayesian inference (in order of publication): Bayesian Simple Linear Regression with Gibbs Sampling in R Blocked Gibbs Sampling in R for Bayesian Multiple Linear Regression Metropolis-in-...

Bayesian Optimization of Machine Learning Models

by Max Kuhn: Director, Nonclinical Statistics, Pfizer Many predictive and machine learning models have structural or tuning parameters that cannot be directly estimated from the data. For example, when using K-nearest neighbor model, there is no anal...

Deep Quantile Regression

Most Deep Learning frameworks currently focus on giving a best estimate as defined by a loss function. Occasionally something beyond a point estimate is required to make a decision. This is where a distribution would be useful. This article will pure...