Showing papers on "Variable-order Bayesian network published in 1983"

Bayesian Equilibrium and Incentive-Compatibility: An Introduction

Abstract: This paper is an introduction to the analysis of games with incomplete information, using a Bayesian model. the logical foundations of the Bayesian model are discussed. To describe rational behavior of players in a Bayesian game, two basic solution concerts are present: Bayesian equilibrium, for games in which the players cannot communicate; and Bayesian incentive-compatibility, for games in which the players can communicate. The concept of virtual utility is developed as a tool for characterizing efficient incentive-compatible coordination mechanisms.

Bayesian analysis of simultaneous equations models

Abstract: Publisher Summary This chapter discusses the Bayesian inference and identification. A Bayesian analysis of the scanning electron microscope (SEM) proceeds along the same lines as any other Bayesian analysis. Thus, if the analyst has chosen to work in a given parameter space, a prior density on that space is defined and Bayes theorem is applied to revise this prior density in the light of available data. The resulting posterior density is then used to solve problems of decision and inference. Predictive densities for future observations can also be derived. The chapter discusses numerical methods for evaluating key characteristics of posterior and predictive density functions. For models with many parameters, such as most simultaneous equation models, analytical methods remain indispensable to evaluate these densities—either fully, or conditionally on a few parameters amenable to numerical treatment, or approximately to construct importance functions for Monte Carlo integration. The classes of prior densities permitting analytical evaluation of the posterior density are limited. In most Bayesian analyses they comprise essentially the so-called noninformative and natural-conjugate families.

Applications of Bayesian Analysis in Econometrics

Abstract: It is an honour to present this paper at St John's College, Cambridge, Sir Harold Jeffreys' college. As you all probably know, Sir Harold has made outstanding, pioneering contributions to the development of Bayesian statistical methodology and applications of it to many problems. In appreciation of his great work, our NBER-NSF Seminar on Bayesian Inference has recently published a book (Zellner, 1980a) honouring him. Jeffreys (1967) set a fine example for us by emphasizing both theory and applications in his work. It is this theme, the interaction between theory and application in Bayesian econometrics, that I shall emphasize in what follows. The rapid growth of Bayesian econometrics from its practically non-existent state in the early 1960s to the present (Zellner, 1981) has involved work on Bayesian inference and decision techniques, applications of them to econometric problems and development of Bayesian computer programs.? Selected applications include Geisel (1970, 1975) who used Bayesian prediction and odds ratios to compare the relative performance of simple Keynesian and Quantity of Money Theory macroeconomic models. Peck (1974) utilized Bayesian estimation techniques in an analysis of investment behaviour of firms in the electric utility industry. Varian (1975) developed and applied Bayesian methods for real estate tax assessment problems. Flood and Garber (1980a, b) applied Bayesian methods in study of monetary reforms using data from the German and several other hyperinflations. Evans (1978) employed posterior odds ratios in a study to determine which of three alternative models best explains the German hyperinflation data. Cooley and LeRoy (1981), Shiller (1973), Zellner and Geisel (1970), and Zellner and Williams (1973) employed a Bayesian approach in study of time series models for US money demand, investment and personal consumption data. Production function models have been analysed from the Bayesian point of view by Sankar (1969), Rossi (1980) and Zellner and Richard (1973). Tsurumi (1976) and Tsurumi and Tsurumi (1981) used Bayesian techniques to analyse structural change problems. Reynolds (1980) developed and applied Bayesian estimation and testing procedures in an analysis of survey data relating to health status, income and other variables. Litterman (1980) has formulated a Bayesian vector autoregressive model that he employed (and is employing) to generate forecasts of major US macroeconomic variables that compare very

Bayesian adaptation in speech recognition

Abstract: In order to achieve state-of-the-art performance in a speaker-dependent speech recognition task, it is necessary to collect a large number of acoustic data samples during the training process. Providing these samples to the system can be a long and tedious process for users. One way to attack this problem is to make use of extra information from a data bank representing a large population of speakers. In this paper we demonstrate that by using Bayesian techniques, prior knowledge derived from speaker-independent data can be combined with speaker-dependent training data to improve system performance.

Advances in Sequential Bayesian Work Sampling

Abstract: Sequential Bayesian work sampling has been previously shown to be both more efficient and more adaptable than traditional methods of work sampling. However, a few deficiencies of the Bayesian approach remained. The advances to that methodology shown here greatly reduce or eliminate those deficiencies. These advances include beta parameter maps, confidence subinterval estimation in closed form, preposterior analysis, estimation methods for remaining sample sizes, and other aids to the management of Bayesian work-sampling studies.

Approximate Bayesian Procedures for Dynamic Linear Models in the Presence of Jumps

Abstract: The paper focuses on the computational difficulties in implementing a coherent Bayesian solution to dynamic linear models which are subject to jumps. Several approximate procedures are suggested and their relative merits are discussed.

Bayesian Models for Response Surfaces of Uncertain Functional Form.

Abstract: : Experimental response functions are often approximated by simple empirical functions such as polynomials. Several methods for modeling such responses which take into account this approximate nature are described and are shown to be essentially equivalent. The models all involve a Bayesian analysis which reflects prior experimnetal belief about the ability of the empirical approximation to represent the true response function. The models are also related to Kalman filters. Implications of the models for statistical inference are examined with particular attention to estimating the response function. Numerical examples help illustrate the models. A general predictive check is developed to examine the consistency of model with the observed data.

Nonparametric bayesian inference for dichotomous response models

Abstract: A Bayesian least squares approach is taken here to estimate certain parameters in generalized linear models for dichotomous response data. The method requires that only first and second moments of the probability distribution representing prior information be specified* Examples are presented to illustrate situations having direct estimates as well as those which require approximate or iterative solutions.