Baysian Estimation on Latent Structure Models for Mixed Measurement Level Data

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The purpose of the research was the followings. The first was proposing latent variable Baysian models for multivariate data with mixed measurement levels. And the second was developing a software that can represent and manipulate various prior information for those models.In this research, the followings were achieved.(1) Prolog (a logic based symbolic programming language) predicates for manipulating fundamental statistical objects (vectors, matrices, relational tables, and discrete categories) were developed. The program depends on SICStus Prolog Object System (developed by Swedish lnstitute of Computer Science).(2) A Baysian framework that can represent topological relations between variables was proposed. The model is an extension of Akaike's (1980) formulation. Also a program that manipulates the complex model structure was developed.(3) The researches on Baysian networks and related statistical models were reviewed. Some theorems were implemented as predicates on Prolog. Statistically equivalent classes of directed acyclic graphs less than or equal to 6^<th> order were determined by the program.Research on latent variable models that can use the prior information for mixed measurement level data was not fully achieved. This is the forthcoming problem.