First Order Markov Chain Model and Rainfall Sequences in several Stations of Spain

2001 ◽  
pp. 417-428
Author(s):  
S. Conejo ◽  
A. Morata ◽  
F. Valero
Keyword(s):  
Markov Chain ◽  
Markov Chain Model ◽  
Chain Model ◽  
First Order ◽  
Order Markov Chain

On the Internal Organization of Consumers' Cognitive Schemata

1989 ◽  
Vol 65 (1) ◽  
pp. 115-126 ◽  
Author(s):  
Roberto Friedmann ◽  
Richard Fox
Keyword(s):  
Markov Chain ◽  
Markov Chain Model ◽  
Operational Definition ◽  
Verbal Stimulus ◽  
Chain Model ◽  
Cognitive Schemata ◽  
First Order ◽  
Internal Organization ◽  
Order Markov Chain ◽  
Made In

An operational definition for internal-based versus external-based schema variables via the tangible versus intangible dichotomy is provided. Using strings of one-word associations made in response to a verbal stimulus, the stochastic structure associated with the use of these variables is investigated. Analysis shows that a first-order Markov chain model which allows for dependence between two consecutive schema variables is more appropriate than a Bernoulli model in the description of the internal organization of cognitive schemata. The phenomena of “chunking” and tangible versus intangible dominance are expressed in the context of the parameters associated with a first-order Markov chain.

MARKOV CHAIN MODEL FOR SEQUENCES OF FIELD WORKDAYS

1974 ◽  
Vol 54 (2) ◽  
pp. 137-148 ◽  
Author(s):  
HENRY HAYHOE ◽  
WOLFGANG BAIER
Keyword(s):  
Markov Chain ◽  
Computer Program ◽  
Markov Chain Model ◽  
Probability Model ◽  
Chain Model ◽  
Sequential Data ◽  
Conditional Probabilities ◽  
Farm Machinery ◽  
First Order ◽  
Order Markov Chain

A computer program was written to estimate from sequential data the parameters for a Markov chain probability model. The program was applied to the analysis of field workday probabilities at 10 selected locations across Canada. The suitability of the first-order Markov chain model was tested and the locations across Canada were compared as to the probability and conditional probabilities of workdays. These probabilities estimated by the program also provide data which should be of interest in farm machinery selection.

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