Showing papers on "Variable-order Bayesian network published in 1973"

Bayesian Models for Forecasting Future Security Prices

Abstract: The field of investment analysis provides an example of a situation in which individuals or corporations make inferences and decisions in the face of uncertainty about future events. The uncertainty concerns future security prices and related variables, and it is necessary to take account of this uncertainty when modeling inferential or decision-making problems relating to investment analysis. Since probability can be thought of as the mathematical language of uncertainty, formal models for decision making under uncertainty require probabilistic inputs. In financial decision making, this is illustrated by the models that have been developed for the portfolio selection problem; such models generally require the assessment of probability distributions (or at least some summary measures of probability distributions) for future prices or returns of the securities that are being considered for inclusion in the portfolio (e.g., see Markowitz [11] and Sharpe [19]).

A Bayesian Optimal Overhaul Interval Model for the Weibull Restoration Process Case

Abstract: This article presents Bayesian estimators of the parameters of the Weibull hazard function in the restoration process case. These estimators are used in a model which optimizes the interval between machine overhauls. Since the properties of the model are not in closed form, a simulation experiment is used to evaluate the effectiveness of the model. The simulation results show that the model does yield lower machine operating costs than the non-Bayesian approach. The effectiveness of the model could be increased by improvements in the quality of the prior estimates used in Bayesian estimation.

Classical error pairs and the Bayesian prior

Abstract: We have attempted to show the way in which Bayesian and classical approaches are both similar and divergent. The vehicle for discussion, primarily, involved considerations of cost consequences, planning horizons and numbers of alternatives. In turn, these variables were related to the statistical problem continuum, with classical and Bayesian approaches at opposite ends of the scale. By relating problem variables to this continuum, the question of which approach is most appropriate in a particular situation can be more readily resolved.

Bayesian Data Analysis of Gambling Preferences

Abstract: : The paper emphasizes the use of Bayesian data analysis for experiments with choices among gambles. In an introductory example, the method is illustrated by a comparison of two learning theories. Special problems arise with the analysis of data from decision making experiments which assume deterministic choice models which cannot be handled by Bayesian analyses. Several ways around these difficulties are suggested, discussed, and demonstrated on two sets of data from choice-among-gambles experiments.