Abstract In this dissertation we will study how extended Poisson process can be applied to construct discrete probabilistic models. An Extended Poisson Process is a continuous time stochastic process with the state space ...

The technology of the DNA-Arrays is a tool used to identify and to compare levels of expression of a great number of genes or fragments of genes, in di¤erent conditions. With this comparison, it is possible to identify ...

This work presents a classical and a Bayesian approaches to two sigmoidal grownth curves, the Gompertz and the Richards models. We consider the homoscedastic assumption and a multiplicative heteroscedastic structure. For ...

In this dissertation we determine maximum likelihood and bayesian estimates of the size of a closed population, from two lists of data of elements of the population. It has been supposed that the registers of the individual ...

This study considers two discrete distributions based on Bernoulli trials: the Binomial and the Negative Binomial. We explore credibility and confidence intervals to estimate the probability of success of each distribution. ...

We propose the split-merge MCMC and birth-split-merge MCMC algorithms to analyse mixture models with an unknown number of components. The strategy for splitting is based on data and posterior distribution. Allocation ...

The goal of this work is to present a classic and a bayesian approach to the Tweedie Compound Poisson model considering the form and application shown at Jorgensen & Souza (1992). A speci.c goal is to estimate the individual ...

In this thesis it is proposed to analyze recurrent event data, recurrent event data with cure fraction and recurrent event data with censoring and competing causes. For the recurrent event data analysis it is proposed a ...

In this work, we introduce a Bayesian Analysis for survival multivariate data in the presence of a covariate vector and censored observations. Different frailties or latent variables are considered to capture the correlation ...