probability model
41Queueing model — In queueing theory, a queueing model is used to approximate a real queueing situation or system, so the queueing behaviour can be analysed mathematically. Queueing models allow a number of useful steady state performance measures to be determined …
42Hidden Markov model — Probabilistic parameters of a hidden Markov model (example) x mdash; states y mdash; possible observations a mdash; state transition probabilities b mdash; output probabilitiesA hidden Markov model (HMM) is a statistical model in which the system …
43Decision tree model — In computational complexity and communication complexity theories the decision tree model is the model of computation or communication in which an algorithm or communication process is considered to be basically a decision tree, i.e., a sequence… …
44Bayesian probability — Bayesian statistics Theory Bayesian probability Probability interpretations Bayes theorem Bayes rule · Bayes factor Bayesian inference Bayesian network Prior · Posterior · Likelihood …
45Simon model — MotivationAiming to account for the wide range of empirical distributions following a power law, Herbert SimonSimon, H. A., 1955, Biometrika 42, 425.] proposed a class of stochastic models that results in a power law distribution function. It… …
46Graphical model — In probability theory, statistics, and machine learning, a graphical model (GM) is a graph that represents independencies among random variables by a graph in which each node is a random variable, and the missing edges between the nodes represent …
47Human information processor model — Human processor model or MHP (Model Human Processor) is a cognitive modeling method used to calculate how long it takes to perform a certain task. Other cognitive modeling methods include parallel design, GOMS, and KLM (human computer… …
48Layered hidden Markov model — The layered hidden Markov model (LHMM) is a statistical model derived from the hidden Markov model (HMM). A layered hidden Markov model (LHMM) consists of N levels of HMMs, where the HMMs on level i + 1 correspond to observation symbols or… …
49Katz's back-off model — Katz back off is a generative n gram language model that estimates the conditional probability of a word given its history in the n gram. It accomplishes this estimation by backing off to models with smaller histories under certain conditions. By …
50Standard Boolean model — The Boolean model of information retrieval (BIR) is a classical information retrieval (IR) model and, at the same time, the first and most adopted one. It is used by virtually all commercial IR systems today. The BIR is based on Boolean Logic and …
