Density estimation is the problem of estimating the probability distribution for a sample of observations from a problem domain. Typically, estimating the entire distribution is intractable, and instead, we are happy to have the expected value of the distribution, such as the mean or mode. Maximum a Posteriori or MAP for short is a Bayesian-based approach to estimating a distribution and model parameters that best explain an observed dataset. This flexible probabilistic framework can be used to provide a Bayesian […]

Last Updated on July 24, 2020 The behavior and performance of many machine learning algorithms are referred to as stochastic. Stochastic refers to a variable process where the outcome involves some randomness and has some uncertainty. It is a mathematical term and is closely related to “randomness” and “probabilistic” and can be contrasted to the idea of “deterministic.” The stochastic nature of machine learning algorithms is an important foundational concept in machine learning and is required to be understand in […]

Last Updated on August 28, 2020 An empirical distribution function provides a way to model and sample cumulative probabilities for a data sample that does not fit a standard probability distribution. As such, it is sometimes called the empirical cumulative distribution function, or ECDF for short. In this tutorial, you will discover the empirical probability distribution function. After completing this tutorial, you will know: Some data samples cannot be summarized using a standard distribution. An empirical distribution function provides a […]