Estimating the Parameters of a Hidden Stochastic Model

A Fruitful Stochastic Model

Contemporary Psychology ◽

10.1037/007600 ◽

1964 ◽

Vol 9 (7) ◽

pp. 273-276

Author(s):

ANATOL RAPOPORT

Keyword(s):

Stochastic Model

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Some thoughts on "A stochastic model for individual choice behaviour"

PsycEXTRA Dataset ◽

10.1037/e627282012-097 ◽

1960 ◽

Author(s):

R. J. Audley

Keyword(s):

Stochastic Model ◽

Individual Choice ◽

Choice Behaviour

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A Stochastic Model for Evolution of Altruistic Genes

Journal de Physique I ◽

10.1051/jp1:1996223 ◽

1996 ◽

Vol 6 (4) ◽

pp. 445-453 ◽

Cited By ~ 4

Author(s):

Roberta Donato

Keyword(s):

Stochastic Model

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A Stochastic Model for the Inheritance of the Cancer Proneness Phenotype

Methods of Information in Medicine ◽

10.1055/s-0038-1636689 ◽

1987 ◽

Vol 26 (03) ◽

pp. 117-123

Author(s):

P. Tautu ◽

G. Wagner

Keyword(s):

Stochastic Model ◽

Gaussian Process ◽

Covariance Function ◽

Neoplastic Disease ◽

Disease Phenotype ◽

Probabilistic Representation ◽

Temporal Behaviour ◽

Continuous Trait ◽

Continuous Parameter ◽

Level Zero

SummaryA continuous parameter, stationary Gaussian process is introduced as a first approach to the probabilistic representation of the phenotype inheritance process. With some specific assumptions about the components of the covariance function, it may describe the temporal behaviour of the “cancer-proneness phenotype” (CPF) as a quantitative continuous trait. Upcrossing a fixed level (“threshold”) u and reaching level zero are the extremes of the Gaussian process considered; it is assumed that they might be interpreted as the transformation of CPF into a “neoplastic disease phenotype” or as the non-proneness to cancer, respectively.

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