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Hierarchical hidden Markov model. 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 ...
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 ...
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 ...
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 ...
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.
Markovian arrival process. In queueing theory, a discipline within the mathematical theory of probability, a Markovian arrival process ( MAP or MArP [1]) is a mathematical model for the time between job arrivals to a system. The simplest such process is a Poisson process where the time between each arrival is exponentially distributed.
The Grossman model of health demand is a model for studying the demand for health and medical care outlined by Michael Grossman in a monograph in 1972 entitled: The demand for health: A theoretical and empirical investigation. The model based demand for medical care on the interaction between a demand function for health and a production ...
In a 1984 paper he introduced the concept of Piecewise deterministic Markov process, a class of Markov models which have been used in many applications in engineering and science. In the early 1990s, Davis introduced the deterministic approach to stochastic control by means of appropriate Lagrange multipliers.