Computational learning theory, or statistical learning theory, refers to mathematical frameworks for quantifying learning tasks and algorithms. These are sub-fields […]

# Archive | Probability

## Develop an Intuition for Bayes Theorem With Worked Examples

Bayes Theorem provides a principled way for calculating a conditional probability. It is a deceptively simple calculation, providing a method […]

## A Gentle Introduction to the Bayes Optimal Classifier

The Bayes Optimal Classifier is a probabilistic model that makes the most probable prediction for a new example. It is […]

## How to Use an Empirical Distribution Function in Python

An empirical distribution function provides a way to model and sample cumulative probabilities for a data sample that does not […]

## What Does Stochastic Mean in Machine Learning?

The behavior and performance of many machine learning algorithms are referred to as stochastic. Stochastic refers to a variable process […]

## A Gentle Introduction to Maximum a Posteriori (MAP) for Machine Learning

Density estimation is the problem of estimating the probability distribution for a sample of observations from a problem domain. Typically, […]

## A Gentle Introduction to Markov Chain Monte Carlo for Probability

Probabilistic inference involves estimating an expected value or density using a probabilistic model. Often, directly inferring values is not tractable […]

## A Gentle Introduction to Monte Carlo Sampling for Probability

Monte Carlo methods are a class of techniques for randomly sampling a probability distribution. There are many problem domains where […]

## A Gentle Introduction to Expectation-Maximization (EM Algorithm)

Maximum likelihood estimation is an approach to density estimation for a dataset by searching across probability distributions and their parameters. […]

## Probabilistic Model Selection with AIC, BIC, and MDL

Model selection is the problem of choosing one from among a set of candidate models. It is common to choose […]