stochastic estimation
1Stochastic optimization — (SO) methods are optimization algorithms which incorporate probabilistic (random) elements, either in the problem data (the objective function, the constraints, etc.), or in the algorithm itself (through random parameter values, random choices,… …
2Stochastic approximation — methods are a family of iterative stochastic optimization algorithms that attempt to find zeroes or extrema of functions which cannot be computed directly, but only estimated via noisy observations. The first, and prototypical, algorithms of this …
3Stochastic volatility — models are used in the field of quantitative finance to evaluate derivative securities, such as options. The name derives from the models treatment of the underlying security s volatility as a random process, governed by state variables such as… …
4Stochastic Frontier Analysis — is a method of economic modeling. It has its starting point in the stochastic production frontier models simultaneously introduced by Aigner, Lovell and Schmidt (1977) and Meeusen and Van den Broeck (1977).The production frontier model without… …
5Estimation of distribution algorithm — Estimation of Distribution Algorithms (EDA), sometimes called Probabilistic Model Building Genetic Algorithms (PMBGA), are an outgrowth of genetic algorithms. In a genetic algorithm, a population of candidate solutions to a problem is maintained… …
6Stochastic kernel estimation — In statistics, a stochastic kernel estimate is an estimate of the transition function of a (usually discrete time) stochastic process. Often, this is an estimate of the conditional density function obtained using kernel density estimation. The… …
7Stochastic grammar — A stochastic grammar (statistical grammar) is a grammar framework with a probabilistic notion of grammaticality: *Stochastic context free grammar *Statistical parsing *Data oriented parsing *Hidden Markov model *Estimation theoryStatistical… …
8Spectral density estimation — In statistical signal processing, the goal of spectral density estimation is to estimate the spectral density (also known as the power spectrum) of a random signal from a sequence of time samples of the signal. Intuitively speaking, the spectral… …
9Maximum entropy spectral estimation — The maximum entropy method applied to spectral density estimation. The overall idea is that the maximum entropy rate stochastic process that satisfies the given constant autocorrelation and variance constraints, is a linear Gauss Markov process… …
10Filtering problem (stochastic processes) — In the theory of stochastic processes, the filtering problem is a mathematical model for a number of filtering problems in signal processing and the like. The general idea is to form some kind of best estimate for the true value of some system,… …
