this article is to develop new methods for inference and prediction in a simple class of stochastic volatility models in which logarithm of conditional volatility follows an autoregressive (AR) times series model. Unlike the autoregressive conditional heteroscedasticity (ARCH) and gener- alized ARCH (GARCH) models [see Bollerslev, Chou, and Kroner (1992) for a survey of ARCH modeling], both the mean and log-volatility equations have separate error terms. The ease of evaluating the ARCH likelihood function and the ability of the ARCH specification to accommodate the timevarying volatility found in many economic time series has fostered an explosion in the use of ARCH models. On the other hand, the likelihood function for stochastic volatility models is difficult to evaluate, and hence these models have had limited empirical application

Abstract not found

Although Bayesian analysis has been in use since Laplace, the Bayesian method of model--comparison has only recently been developed in depth. In this paper, the Bayesian approach to regularisation and model--comparison is demonstrated by studying the inference problem of interpolating noisy data

in das Bayesian Analysis Toolkit

generalization of Jeffreys’s ideas is to explicitly include model checking in the process of data analysis.

After making some general remarks, I consider two examples that illustrate the use of Bayesian Probability Theory. The first is a simple one, the physicist’s favourite ‘toy, ’ that provides a forum for a discussion of the key conceptual issue of Bayesian analysis: the assignment of prior

Accurate traffic classification is of fundamental importance to numerous other network activities, from security monitoring to accounting, and from Quality of Service to providing operators with useful forecasts for long-term provisioning. We apply a Naïve Bayes estimator to categorize traffic by application. Uniquely, our work capitalizes on hand-classified network data, using it as input to a supervised Naïve Bayes estimator. In this paper we illustrate the high level of accuracy achievable with the Naïve Bayes estimator. We further illustrate the improved accuracy of refined variants of this estimator. Our results indicate that with the simplest of Naïve Bayes estimator we are able to achieve about 65 % accuracy on per-flow classification and with two powerful refinements we can improve this value to better than 95%; this is a vast improvement over traditional techniques that achieve 50–70%. While our technique uses training data, with categories derived from packet-content, all of our training and testing was done using header-derived discriminators. We emphasize this as a powerful aspect of our approach: using samples of well-known traffic to allow the categorization of traffic using commonly-available information alone.

This paper reviews Bayesian methods that have been developed in recent years to estimate and evaluate dynamic stochastic general equilibrium (DSGE) models. We consider the estimation of linearized DSGE models, the evaluation of models based on Bayesian model checking, posterior odds comparisons

In this paper we present anaverage-case analysis of the Bayesian classifier, a simple induction algorithm that fares remarkably well on many learning tasks. Our analysis assumes a monotone conjunctive target concept, and independent, noise-free Boolean attributes. We calculate the probability

As the usual normality assumption is firmly rejected by the data, investors encounter a data-generating process (DGP) uncertainty in making investment decisions. In this paper, we propose a novel way of incorporating uncertainty about the DGP into portfolio analysis. We find that accounting for fat