Nature

A novel Bayesian network inference algorithm for integrative analysis of heterogeneous deep sequencing data

Next Generation Sequencing (NGS) technology has enabled sequencing millions of short DNA tags in a single pass. NGS-based techniques such as ChIP-Seq/BS-Seq (Chromatin Immunoprecipitation/Bisulfite ...
Diginomica

Can Bayesian Networks provide answers when Machine Learning comes up short? It's a question of probabilities

Machine Learning gets all the marketing hype, but are we overlooking Bayesian Networks? Here's a deeper look at why "Bayes Nets" are underrated - especially when it comes to addressing probability and ...
JSTOR Daily

Bayesian inference for the mixed conditional heteroskedasticity model

We estimate by Bayesian inference the mixed conditional heteroskedasticity model of Haas et al. (2004a Journal of Financial Econometrics 2, 211–50). We construct a Gibbs sampler algorithm to compute ...
JSTOR Daily

Learning from a lot: Empirical Bayes for high-dimensional model-based prediction

Empirical Bayes is a versatile approach to “learn from a lot” in two ways: first, from a large number of variables and, second, from a potentially large amount of prior information, for example, ...
lse

Bayesian Inference

This course is available on the BSc in Actuarial Science, BSc in Actuarial Science (with a Placement Year), BSc in Data Science, BSc in Mathematics with Data Science, BSc in Mathematics with Economics ...
technologynetworks

An Introduction to Bayesian Statistics

In the ever-evolving toolkit of statistical analysis techniques, Bayesian statistics has emerged as a popular and powerful methodology for making decisions from data in the applied sciences. Bayesian ...