In In this paper, we introduce two convergent Monte Carlo algorithms for optimizing complex stochastic systems. The first algorithm, which is applicable to to regenerative processes, operates by estimating finite differences. The second method is of Robbins-Monro type and is applicable to to Markov chains. The algorithm is driven by derivative estimates obtained via a likelihood ratio argument. 1.

In this paper we propose the beta slashed generalised half-normal distribution, which includes some important distributions such as half-normal, and distributions. Explicit expressions for cumulative distribution characteristic functions are derived. The maximum likelihood estimates of parameters obtained via EM algorithm value proposed model is illustrated with an application on fatigue data. ...

The application of Kalman filtering methods and maximum likelihood parameter estimation to models of commodity prices and futures prices has been considered by several authors. The usual method of finding the maximum likelihood parameter estimates (MLEs) is to numerically maximize the likelihood function. We present, as an alternative to numerical maximization of the likelihood, a filter-based ...

Consider a problem of predicting a response variable using a set of covariates in a linear regression model. If it is a priori known or suspected that a subset of the covariates do not significantly contribute to the overall fit of the model, a restricted model that excludes these covariates, may be sufficient. If, on the other hand, the subset provides useful information, shrinkage meth...

This paper presents a new method of feature dimension reduction in hidden Markov modeling (HMM) for speech recognition. The key idea is to apply reduced rank maximum likelihood estimation in the M-step of the usual Baum-Welch algorithm for estimating HMM parameters such that the estimates of the Gaussian distribution parameters are restricted in a sub-space of reduced dimensionality. There are ...

What does the posterior probability of a phylogenetic tree mean?This simulation study shows that Bayesian posterior probabilities have the meaning that is typically ascribed to them; the posterior probability of a tree is the probability that the tree is correct, assuming that the model is correct. At the same time, the Bayesian method can be sensitive to model misspecification, and the sensiti...