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Conditional probability distribution. In probability theory and statistics, the conditional probability distribution is a probability distribution that describes the probability of an outcome given the occurrence of a particular event. Given two jointly distributed random variables and , the conditional probability distribution of given is the ...
The Kaplan–Meier estimator, [1][2] also known as the product limit estimator, is a non-parametric statistic used to estimate the survival function from lifetime data. In medical research, it is often used to measure the fraction of patients living for a certain amount of time after treatment. In other fields, Kaplan–Meier estimators may be ...
Conditional probability table. In statistics, the conditional probability table (CPT) is defined for a set of discrete and mutually dependent random variables to display conditional probabilities of a single variable with respect to the others (i.e., the probability of each possible value of one variable if we know the values taken on by the ...
Conditional expectation. In probability theory, the conditional expectation, conditional expected value, or conditional mean of a random variable is its expected value evaluated with respect to the conditional probability distribution. If the random variable can take on only a finite number of values, the "conditions" are that the variable can ...
Fisher's exact test is a statistical significance test used in the analysis of contingency tables. [1][2][3] Although in practice it is employed when sample sizes are small, it is valid for all sample sizes. It is named after its inventor, Ronald Fisher, and is one of a class of exact tests, so called because the significance of the deviation ...
The survival function is a function that gives the probability that a patient, device, or other object of interest will survive past a certain time. [1] The survival function is also known as the survivor function[2] or reliability function. [3] The term reliability function is common in engineering while the term survival function is used in a ...
The posterior probability is a type of conditional probability that results from updating the prior probability with information summarized by the likelihood via an application of Bayes' rule. [1] From an epistemological perspective, the posterior probability contains everything there is to know about an uncertain proposition (such as a ...
Formally, a regular conditional probability is defined as a function called a "transition probability", where: For every , is a probability measure on . Thus we provide one measure for each . where is the pushforward measure of the distribution of the random element , i.e. the support of the . Specifically, if we take , then , and so.