probability model
121Zimm-Bragg model — In statistical mechanics, the Zimm Bragg model is a helix coil transition model that describes helix coil transitions of macromolecules, usually polymer chains. Most models provide a reasonable approximation of the fractional helicity of a given… …
122CALS Table Model — The CALS Table Model is a standard for representing tables in SGML/XML. It was developed as part of the CALS DOD initiative.History and RationaleThe CALS Table Model was developed by the CALS Industry Steering Group Electronic Publishing… …
123Cellular Potts model — The cellular Potts model is a lattice based computational modeling method to simulate the collective behavior of cellular structures. Other names for the CPM are extended large q Potts model and Glazier and Graner model. First developed by James… …
124Heston model — In finance, the Heston model is a mathematical model describing the evolution of the volatility of an underlying asset. It is a stochastic volatility model: such a model assumes that the volatility of the asset is not constant, nor even… …
125Mixed mating model — The mixed mating model is a mathematical model that describes the mating system of a plant population in terms of the degree of self fertilisation present. It is a fairly simplistic model, employing several simplifying assumptions, most notably… …
126Trauma model of mental disorders — Trauma models of mental disorder (alternatively called trauma models of psychopathology) emphasise the effects of psychological trauma, particularly in early development, as the key causal factor in the development of some or many psychiatric… …
127Calibrated probability assessment — Calibrated probability assessments are subjective probabilities assigned by individuals who have been trained to assess probabilities in a way that historically represents their uncertainty [S. Lichtenstein, B. Fischhoff, and L. D. Phillips,… …
128Marginal model — In statistics, marginal models (Heagerty Zeger, 2000) are a technique for obtaining regression estimates in multilevel modeling, also called hierarchical linear models. People often want to know the effect of a predictor/explanatory variable X,… …
