Bayesian Inference and Deep Learning: Theory and Practice of Generative Models

IntroductionPurpose of this bookThis book depicts the path from Bayesian inference to deep learning as a single long-form technical volume. There is one central theme: how can we handle uncertainty in ...
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 ...

4 Essential Books on R, Bayesian Methods, and Reinforcement Learning

Introduction The moment I felt the most cold sweat in my machine learning career was during an internal review when I was ...
ascopubs.org

Identifying Confounders Using Bayesian Networks and Estimating Treatment Effect in Prostate Cancer With Observational Data

Randomized controlled trials are considered the golden standard for estimating treatment effect but are costly to perform and not always possible. Observational data, although readily available, is ...
Diginomica

How can Bayesien Inference support complex decisions? A practical guide to an overlooked approach

In my practice, I find most people involved with advanced analytics, such as predictive, data science, and ML, are familiar with the name Bayes, and can even reproduce the simple theorem below. Still, ...
techtarget

Bayesian networks applications are fueling enterprise support

Bayesian networks have become popular tools for enterprise data scientists working with prediction, as the rise of cheap and abundant cloud computing has made way for adaptable infrastructure. In the ...