Learn how to calculate, apply, and experiment with conditional probability in your field of work. Conditional probability can help you understand data and make predictions.

Conditional probability example. We roll two fair 6-sided dice. Each one of the 36 possible outcomes is assumed to be equally likely. (a) Find the probability that doubles are rolled (i.e., both dice ...

Bayes' theorem is a formula for calculating the probability of an event. Learn how to calculate Bayes' theorem and see examples.

Understanding conditional probability is essential when exploring fields in Machine Learning and Artificial Intelligence. In this lesson, you'll learn about conditional probability, what it is, and ...

Thomas Bayes and Pierre-Simon Laplace were two pioneers in the world of probability theory. Bayes developed Bayesian Probability, Bayesian Reasoning, and the Bayes’ Theorem, and Laplace is often ...

Probability theorem gets quantum makeoverWhat would Thomas Bayes think? In 1763, he proposed a new approach to calculate ...

Possibility Theory and Conditional Probability Publication Trend The graph below shows the total number of publications each year in Possibility Theory and Conditional Probability.

We adapt the expectation-maximization algorithm to incorporate unobserved heterogeneity into conditional choice probability (CCP) estimators of dynamic discrete choice problems. The unobserved ...

Joint probability is a statistical measure that calculates the likelihood of two events occurring together and at the same point in time.