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26 relations: Bayes' theorem, Bayesian statistics, Bayesian structural time series, Bernstein–von Mises theorem, Bertrand's box paradox, Conditional probability, Conditional probability distribution, Credible interval, Event (probability theory), John Wiley & Sons, Law of total probability, Likelihood function, Monty Hall problem, Normalizing constant, Prediction interval, Prior probability, Probabilistic classification, Probability density function, Probability distribution, Probability distribution function, Probability of success, Random variable, Scientific evidence, Spike-and-slab variable selection, Statistical classification, Three Prisoners problem.
Bayes' theorem
In probability theory and statistics, Bayes’ theorem (alternatively Bayes’ law or Bayes' rule, also written as Bayes’s theorem) describes the probability of an event, based on prior knowledge of conditions that might be related to the event.
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Bayesian statistics
Bayesian statistics, named for Thomas Bayes (1701–1761), is a theory in the field of statistics in which the evidence about the true state of the world is expressed in terms of degrees of belief known as Bayesian probabilities.
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Bayesian structural time series
Bayesian structural time series (BSTS) model is a machine learning technique used for feature selection, time series forecasting, nowcasting, inferring causal impact and other.
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Bernstein–von Mises theorem
In Bayesian inference, the Bernstein–von Mises theorem provides the basis for the important result that the posterior distribution for unknown quantities in any problem is effectively asymptotically independent of the prior distribution (assuming it obeys Cromwell's rule) as the data sample grows large.
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Bertrand's box paradox
Bertrand's box paradox is a paradox of elementary probability theory, first posed by Joseph Bertrand in his 1889 work Calcul des probabilités.
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Conditional probability
In probability theory, conditional probability is a measure of the probability of an event (some particular situation occurring) given that (by assumption, presumption, assertion or evidence) another event has occurred.
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Conditional probability distribution
In probability theory and statistics, given two jointly distributed random variables X and Y, the conditional probability distribution of Y given X is the probability distribution of Y when X is known to be a particular value; in some cases the conditional probabilities may be expressed as functions containing the unspecified value x of X as a parameter.
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Credible interval
In Bayesian statistics, a credible interval is a range of values within which an unobserved parameter value falls with a particular subjective probability.
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Event (probability theory)
In probability theory, an event is a set of outcomes of an experiment (a subset of the sample space) to which a probability is assigned.
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John Wiley & Sons
John Wiley & Sons, Inc., also referred to as Wiley, is a global publishing company that specializes in academic publishing.
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Law of total probability
In probability theory, the law (or formula) of total probability is a fundamental rule relating marginal probabilities to conditional probabilities.
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Likelihood function
In frequentist inference, a likelihood function (often simply the likelihood) is a function of the parameters of a statistical model, given specific observed data.
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Monty Hall problem
The Monty Hall problem is a brain teaser, in the form of a probability puzzle, loosely based on the American television game show Let's Make a Deal and named after its original host, Monty Hall.
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Normalizing constant
The concept of a normalizing constant arises in probability theory and a variety of other areas of mathematics.
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Prediction interval
In statistical inference, specifically predictive inference, a prediction interval is an estimate of an interval in which a future observation will fall, with a certain probability, given what has already been observed.
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Prior probability
In Bayesian statistical inference, a prior probability distribution, often simply called the prior, of an uncertain quantity is the probability distribution that would express one's beliefs about this quantity before some evidence is taken into account.
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Probabilistic classification
In machine learning, a probabilistic classifier is a classifier that is able to predict, given an observation of an input, a probability distribution over a set of classes, rather than only outputting the most likely class that the observation should belong to.
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Probability density function
In probability theory, a probability density function (PDF), or density of a continuous random variable, is a function, whose value at any given sample (or point) in the sample space (the set of possible values taken by the random variable) can be interpreted as providing a relative likelihood that the value of the random variable would equal that sample.
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Probability distribution
In probability theory and statistics, a probability distribution is a mathematical function that provides the probabilities of occurrence of different possible outcomes in an experiment.
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Probability distribution function
A probability distribution function is some function that may be used to define a particular probability distribution.
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Probability of success
The probability of success (POS) is a statistics concept commonly used in the pharmaceutical industry including by health authorities to support decision making.
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Random variable
In probability and statistics, a random variable, random quantity, aleatory variable, or stochastic variable is a variable whose possible values are outcomes of a random phenomenon.
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Scientific evidence
Scientific evidence is evidence which serves to either support or counter a scientific theory or hypothesis.
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Spike-and-slab variable selection
Spike-and-slab regression is a Bayesian variable selection technique that is particularly useful when the number of possible predictors is larger than the number of observations.
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Statistical classification
In machine learning and statistics, classification is the problem of identifying to which of a set of categories (sub-populations) a new observation belongs, on the basis of a training set of data containing observations (or instances) whose category membership is known.
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Three Prisoners problem
The Three Prisoners problem appeared in Martin Gardner's "Mathematical Games" column in Scientific American in 1959.
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References
[1] https://en.wikipedia.org/wiki/Posterior_probability
