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Markov model. In probability theory, a Markov model is a stochastic model used to model pseudo-randomly changing systems. [1] It is assumed that future states depend only on the current state, not on the events that occurred before it (that is, it assumes the Markov property ). Generally, this assumption enables reasoning and computation with ...
The hierarchical hidden Markov model (HHMM) is a statistical model derived from the hidden Markov model (HMM). In an HHMM, each state is considered to be a self-contained probabilistic model. More precisely, each state of the HHMM is itself an HHMM. HHMMs and HMMs are useful in many fields, including pattern recognition.
Markov decision process. In mathematics, a Markov decision process ( MDP) is a discrete-time stochastic control process. It provides a mathematical framework for modeling decision making in situations where outcomes are partly random and partly under the control of a decision maker. MDPs are useful for studying optimization problems solved via ...
The process is Markovian only at the specified jump instants, justifying the name semi-Markov. (See also: hidden semi-Markov model.) A semi-Markov process (defined in the above bullet point) in which all the holding times are exponentially distributed is called a continuous-time Markov chain. In other words, if the inter-arrival times are ...
Baum–Welch algorithm. In electrical engineering, statistical computing and bioinformatics, the Baum–Welch algorithm is a special case of the expectation–maximization algorithm used to find the unknown parameters of a hidden Markov model (HMM). It makes use of the forward-backward algorithm to compute the statistics for the expectation step.
Maslow's hierarchy of needs is an idea in psychology proposed by American psychologist Abraham Maslow in his 1943 paper "A Theory of Human Motivation" in the journal Psychological Review. [1] Maslow subsequently extended the idea to include his observations of humans' innate curiosity. His theories parallel many other theories of human ...
D. G. Champernowne built a Markov chain model of the distribution of income in 1953. Herbert A. Simon and co-author Charles Bonini used a Markov chain model to derive a stationary Yule distribution of firm sizes. Louis Bachelier was the first to observe that stock prices followed a random walk.
Hidden Markov model. A hidden Markov model ( HMM) is a Markov model in which the observations are dependent on a latent (or "hidden") Markov process (referred to as ). An HMM requires that there be an observable process whose outcomes depend on the outcomes of in a known way.