Explain why probability is important to statistics and data science. See the relationship between conditional and independent events in a statistical experiment. Calculate the expectation and variance ...

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

The world is full of uncertainty: accidents, storms, unruly financial markets, noisy communications. The world is also full of data. Build foundational knowledge of data science with this introduction ...

Nonparametric maximum likelihood estimation of the probability of failing from a particular cause by time t in the presence of other acting causes (i.e., the cause-specific failure probability) is ...

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

An international team of researchers have successfully derived a quantum version of Bayes’ rule, a cornerstone of probability theory ...

A conditional resampling methodology is proposed for improving the estimators of multinomial classification probabilities in the presence of a fallible classifier.