Tags » Bayesian

Bayesian Simple Linear Regression with Gibbs Sampling in R

Many introductions to Bayesian analysis use relatively simple didactic examples (e.g. making inference about the probability of success given bernoulli data). While this makes for a good introduction to Bayesian principles, the extension of these principles to regression is not straight-forward. 1,040 more words

R

TL;DR

Last week I collected letters (323 letter forms)  from open-tamil and estimated the unigram, bigrams and trigram frequencies in a given Tamil lexicon with about 65,0000 odd words. 444 more words

Computing

Graphical posterior predictive classifier: Bayesian model averaging with particle Gibbs

Tatjana Pavlenko, Felix Leopoldo Rios

(Submitted on 21 Jul 2017 (v1), last revised 25 Jul 2017 (this version, v2))

In this study, we present a multi-class graphical Bayesian predictive classifier that incorporates the uncertainty in the model selection into the standard Bayesian formalism. 170 more words

Machine Learning Frontier

Shallow Understanding on Bayesian Optimization

Ramraj Chandradevan

Bayesian Optimization is a method that uses some kind of approximation. Imagine, if we don’t know a function, what we usually do? Ofcourse, we will try to guess or approximate it with some know prior knowledge. 90 more words

Machine Learning Frontier

A practical explanation of a Naive Bayes classifier

The simplest solutions are usually the most powerful ones, and Naive Bayes is a good proof of that. In spite of the great advances of the Machine Learning in the last years, it has proven to not only be simple but also fast, accurate and reliable. 184 more words

Programming

Schedule Risk: Event Chain Methodology

I want you to think about time estimates for a moment, specifically, let’s talk about something we all enjoy: our commute. The last significant commute I had was 70 miles each way through two metropolitan traffic patterns. 1,318 more words

Subjectivity and Risk Scoring

One common misconception that I was guilty of subscribing to is that while qualitative risk analysis always has some degree of subjectivity, quantitative risk analysis should remain strictly objective. 853 more words