Kaplan, A. I., & Borodovskii, M. I. (1989). [Alternative animal behavior: a model and its statistical characteristics]. Nauchnye Doki Vyss Shkoly Biol Nauki, (3), 29–32.
Abstract: The rats' alternative behaviour in T-maze at simultaneous two-sided food refreshment in 13 trials a day during 6 days has been studied. It has been found that in the first testing days the indexes of alternative behaviour of animals correspond to the characteristics of the random alternation. However, on the 5-6th day of testing in the overwhelming majority of rats the true deviation of alternation index above or below than the theoretical values has been revealed. A question on the existence of two strategies of cognitive behaviour alteration and perseveration in rat population is under discussion.
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Pattison, P., & Wasserman, S. (1999). Logit models and logistic regressions for social networks: II. Multivariate relations. Br J Math Stat Psychol, 52 ( Pt 2), 169–193.
Abstract: The research described here builds on our previous work by generalizing the univariate models described there to models for multivariate relations. This family, labelled p*, generalizes the Markov random graphs of Frank and Strauss, which were further developed by them and others, building on Besag's ideas on estimation. These models were first used to model random variables embedded in lattices by Ising, and have been quite common in the study of spatial data. Here, they are applied to the statistical analysis of multigraphs, in general, and the analysis of multivariate social networks, in particular. In this paper, we show how to formulate models for multivariate social networks by considering a range of theoretical claims about social structure. We illustrate the models by developing structural models for several multivariate networks.
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Thompson, J. A., Brown, S. E. 2nd, Riddle, W. T., Seahorn, J. C., & Cohen, N. D. (2005). Use of a Bayesian risk-mapping technique to estimate spatial risks for mare reproductive loss syndrome in Kentucky. Am J Vet Res, 66(1), 17–20.
Abstract: OBJECTIVE: To estimate spatial risks associated with mare reproductive loss syndrome (MRLS) during 2001 among horses in a specific study population and partition the herd effects into those attributable to herd location and those that were spatially random and likely attributable to herd management. Animals-Pregnant broodmares from 62 farms in 7 counties in central Kentucky. PROCEDURE: Veterinarians provided the 2001 abortion incidence proportions for each farm included in the study. Farms were georeferenced and data were analyzed by use of a fully Bayesian risk-mapping technique. RESULTS: Large farm-to-farm variation in MRLS incidence proportions was identified. The farm-to-farm variation was largely attributed to spatial location rather than to spatially random herd effects. CONCLUSIONS AND CLINICAL RELEVANCE: Results indicate that there are considerable data to support an ecologic cause and potential ecologic risk factors for MRLS. Veterinary practitioners with more detailed knowledge of the ecology in the 7 counties in Kentucky that were investigated may provide additional data that would assist in the deduction of the causal factor of MRLS via informal geographic information systems analyses and suggest factors for inclusion in further investigations.
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