Fundamental Basics of the CPBS Approach

In this chapter we will examine four basic methodologies and decision tools that are utilized in the CPBS approach to decision making. The first is Bayesian decision analysis, which forms the heart of the CPBS approach. Tests and measurements that are used to identify or detect a property of interest are generally not perfect. When tests are biased or inaccurate, it is often advantageous to use more than one test. The interpretation of a combination of test results can be problematic because there often exists a variable amount of information overlap (positive dependence) and differences (negative dependence) among the tests. It is a difficult problem to account for both the imperfection of the individual tests as well as their interdependencies in their joint interpretation.

[1]  J. Berger Statistical Decision Theory and Bayesian Analysis , 1988 .

[2]  Yacov Y. Haimes,et al.  Multiobjective Decision Making: Theory and Methodology , 1983 .

[3]  H S Rosenkranz,et al.  Cluster analysis in predicting the carcinogenicity of chemicals using short-term assays. , 1985, Mutation research.

[4]  Philip Wolfe,et al.  Contributions to the theory of games , 1953 .

[5]  R. S. Laundy,et al.  Multiple Criteria Optimisation: Theory, Computation and Application , 1989 .

[6]  George L. Nemhauser,et al.  Introduction To Dynamic Programming , 1966 .

[7]  Stuart E. Dreyfus,et al.  Applied Dynamic Programming , 1965 .

[8]  M. Shelly,et al.  HUMAN JUDGMENTS AND OPTIMALITY.. , 1966 .

[9]  W. D. Ray The Foundation of Statistical Inference , 1963 .

[10]  L. J. Savage,et al.  The Foundation of Statistics , 1956 .

[11]  Michael D. Intriligator,et al.  Mathematical optimization and economic theory , 1971 .

[12]  Yacov Y. Haimes,et al.  Multiobjective optimization in water resources systems : the surrogate worth trade-off method , 1975 .

[13]  M. Weinstein,et al.  Clinical Decision Analysis , 1980 .

[14]  D. W. Roncek,et al.  Discrete Discriminant Analysis. , 1979 .

[15]  Michael R. Anderberg,et al.  Cluster Analysis for Applications , 1973 .

[16]  C. West Churchman,et al.  The Systems Approach , 1979 .

[17]  Robert F. Ling,et al.  On the theory and construction of k-clusters , 1972, Comput. J..

[18]  O. H. Brownlee,et al.  ACTIVITY ANALYSIS OF PRODUCTION AND ALLOCATION , 1952 .

[19]  R. F. Ling A Probability Theory of Cluster Analysis , 1973 .

[20]  Robert L. Winkler,et al.  An Introduction to Bayesian Inference and Decision , 1972 .

[21]  J. T. Buchanan,et al.  Discrete and Dynamic Decision Analysis , 1982 .

[22]  H. T. Clifford,et al.  An Introduction to Numerical Classification. , 1976 .

[23]  P. Moore The Mythical Threat of Bayesianism , 1978 .

[24]  R. McKelvey,et al.  A statistical model for the analysis of ordinal level dependent variables , 1975 .

[25]  J. Cohon Multiobjective optimization in water resources systems , 1976 .

[26]  Howard Raiffa,et al.  Decision analysis: introductory lectures on choices under uncertainty. 1968. , 1969, M.D.Computing.

[27]  William S. Meisel,et al.  Computer-oriented approaches to pattern recognition , 1972 .

[28]  Y. Haimes,et al.  Multiobjectives in water resource systems analysis: The Surrogate Worth Trade Off Method , 1974 .

[29]  Allan Easton,et al.  Complex Managerial Decisions Involving Multiple Objectives , 1973 .

[30]  J. Neumann,et al.  Theory of games and economic behavior , 1945, 100 Years of Math Milestones.

[31]  H S Rosenkranz,et al.  The carcinogenicity prediction and battery selection (CPBS) method: a Bayesian approach. , 1985, Mutation research.

[32]  Anil K. Jain,et al.  Clustering Methodologies in Exploratory Data Analysis , 1980, Adv. Comput..

[33]  H S Rosenkranz,et al.  Prediction of environmental carcinogens: a strategy for the mid-1980s. , 1984, Environmental mutagenesis.

[34]  H. C. Hamaker Bayesianism; a Threat to the Statistical Profession? , 1977 .