A few weeks ago I wrote about Kuhn’s theory of paradigm shifts and how it relates to Bayesian inference. In this post I want to back up a little bit and explain what Bayesian inference is, and eventually rediscover the idea of a paradigm shift just from understanding how Bayesian inference works.
Bayes Theorem provides a principled way for calculating a conditional probability. It is a deceptively simple calculation, although it can be used to easily calculate the conditional probability of events where intuition often fails. Bayes Theorem also provides a way for thinking about the evaluation and selection of different models for a given dataset in …
In this post we’ll explore how we can derive logistic regression from Bayes’ Theorem. Starting with Bayes’ Theorem we’ll work our way to computing the log odds of our problem and the arrive at the inverse logit function. After reading this post you’ll have a much stronger intuition for how logistic
OpenMarkov is a software tool for probabilistic graphical models (PGMs) developed by the Research Centre for Intelligent Decision-Support Systems of the UNED in Madrid, Spain.
It has been designed for:
- editing and evaluating several types of several types of PGMs, such as Bayesian networks, influence diagrams, factored Markov models, etc.;
- learning Bayesian networks from data interactively;
- cost-effectiveness analysis.