Math & techniques
This page dedicates to a general-purpose machine learning technique called Maximum Entropy Modeling (MaxEnt for short).
A simple Bayesian network. Rain influences whether the sprinkler is activated, and both rain and the sprinkler influence whether the grass is wet. A Bayesian network , Bayes network , belief network , Bayes(ian) model or probabilistic directed acyclic graphical model is a probabilistic graphical model (a type of statistical model ) that represents a set of random variables and their conditional dependencies via a directed acyclic graph (DAG). For example, a Bayesian network could represent the probabilistic relationships between diseases and symptoms.
In this introduction, we will briefly discuss those elementary statistical concepts that provide the necessary foundations for more specialized expertise in any area of statistical data analysis. The selected topics illustrate the basic assumptions of most statistical methods and/or have been demonstrated in research to be necessary components of our general understanding of the "quantitative nature" of reality (Nisbett, et al., 1987).
In statistics , Bayesian inference is a method of inference in which Bayes' rule is used to update the probability estimate for a hypothesis as additional evidence is learned. Bayesian updating is an important technique throughout statistics, and especially in mathematical statistics : For some cases, exhibiting a Bayesian derivation for a statistical method automatically ensures that the method works as well as any competing method. Bayesian updating is especially important in the dynamic analysis of a sequence of data . Bayesian inference has found application in a range of fields including science , engineering , medicine , and law . In the philosophy of decision theory , Bayesian inference is closely related to discussions of subjective probability, often called " Bayesian probability ".