Dynamic programming
In mathematics, computer science, economics, and bioinformatics, dynamic programming is a method for solving a complex problem by breaking it down into a collection of simpler subproblems. It is appli...
Loss function
In mathematical optimization, statistics, decision theory and machine learning, a loss function or cost function is a function that maps an event or values of one or more variables onto a real number ...
Response surface methodology
In statistics, response surface methodology (RSM) explores the relationships between several explanatory variables and one or more response variables. The method was introduced by G. E. P. Box and K....
Expenditure minimization problem
In microeconomics, the expenditure minimization problem is another perspective on the utility maximization problem: "how much money do I need to reach a certain level of happiness?". This question co...
Backward induction
Backward induction is the process of reasoning backwards in time, from the end of a problem or situation, to determine a sequence of optimal actions. It proceeds by first considering the last time a d...
Mean squared prediction error
In statistics the mean squared prediction error of a smoothing or curve fitting procedure is the expected value of the squared difference between the fitted values and the (unobservable) function g. ...
Odds algorithm
The odds-algorithm is a mathematical method for computing optimalstrategies for a class of problems that belong to the domain of optimal stopping problems. Their solution follows from the odds-strat...
St. Petersburg paradox
The St. Petersburg lottery or St. Petersburg paradox is a paradox related to probability and decision theory in economics. It is based on a particular (theoretical) lottery game that leads to a random...
Maximum subarray problem
In computer science, the maximum subarray problem is the task of finding the contiguous subarray within a one-dimensional array of numbers (containing at least one positive number) which has the large...
Generalized expected utility
The expected utility model developed by John von Neumann and Oskar Morgenstern dominated decision theory from its formulation in 1944 until the late 1970s, not only as a prescriptive, but also as a de...
Expected utility hypothesis
In economics, game theory, and decision theory the expected utility hypothesis refers to a hypothesis concerning people's preferences with regard to choices that have uncertain outcomes (gambles). Thi...
Regret (decision theory)
Regret is the negative emotion experienced when learning that an alternative course of action would have resulted in a more favorable outcome. The theory of regret aversion or anticipated regret propo...
Viterbi algorithm
The Viterbi algorithm is a dynamic programming algorithm for finding the most likely sequence of hidden states – called the Viterbi path – that results in a sequence of observed events, es...
Huber loss function
In statistics, the Huber loss is a loss function used in robust regression, that is less sensitive to outliers in data than the squared error loss. A variant for classification is also sometimes used....
Sum of absolute transformed differences
Sum of absolute transformed differences (SATD) is a widely used video quality metric used for block-matching in motion estimation for video compression. It works by taking a frequency transform, usua...
Stopping time
In probability theory, in particular in the study of stochastic processes, a stopping time (also Markov time) is a specific type of “random time”: a random variable whose value is interpreted as the t...
Optimal decision
An optimal decision is a decision such that no other available decision options will lead to a better outcome. It is an important concept in decision theory. In order to compare the different decision...
Mabinogion sheep problem
In probability theory, the Mabinogion sheep problem or Mabinogian urn is a problem in stochastic control introduced by David Williams (1991, 15.3), who named it after a herd of magic sh...
Automatic basis function construction
Automatic basis function construction (or basis discovery) is the method of looking for a set of task-independent basis functions that map the state space to a lower-dimensional embedding, while still...
Markov decision process
Markov decision processes (MDPs), named after Andrey Markov, provide a mathematical framework for modeling decision making in situations where outcomes are partly random and partly under the control o...
Dynamic time warping
In time series analysis, dynamic time warping (DTW) is an algorithm for measuring similarity between two temporal sequences which may vary in time or speed. For instance, similarities in walking patt...
Nuisance parameter
In statistics, a nuisance parameter is any parameter which is not of immediate interest but which must be accounted for in the analysis of those parameters which are of interest. The classic example o...
Optimal design
In the design of experiments, optimal designs are a class of experimental designs that are optimal with respect to some statistical criterion. The creation of this field of statistics has been credite...
Bayesian experimental design
Bayesian experimental design provides a general probability-theoretical framework from which other theories on experimental design can be derived. It is based on Bayesian inference to interpret the ob...
Hinge loss
In machine learning, the hinge loss is a loss function used for training classifiers. The hinge loss is used for "maximum-margin" classification, most notably for support vector machines (SVMs).For an...
Cost–utility analysis
Cost–utility analysis (CUA) is a form of financial analysis used to guide procurement decisions. The most common and well-known application of this analysis is in pharmacoeconomics, especially health...
Mean squared error
In statistics, the mean squared error (MSE) of an estimator measures the average of the squares of the "errors", that is, the difference between the estimator and what is estimated. MSE is a risk fu...
System identification
The field of system identification uses statistical methods to build mathematical models of dynamical systems from measured data. System identification also includes the optimal design of experiments...
Bitonic tour
In computational geometry, a bitonic tour of a set of point sites in the Euclidean plane is a closed polygonal chain that has each site as one of its vertices, such that any vertical line crosses the ...