Improving Hierarchical Monte

Improving the Hierarachical Stochastic Radiosity Algorithm

Hierarchical subdivision techniques remove the need for a-priori meshing of surfaces when approximating global illumination. In addition they allow progressive re nement of the solution. However, when subdivision is based upon Monte Carlo methods, due to the stochastic nature of such techniques, subdivision decisions cannot be made unless a su ciently large number of samples have been considere...

Improving the efficiency of Monte Carlo power estimation [VLSI]

In this paper, we propose two efficient statistical sampling techniques for estimating the total power consumption of large hierarchical circuits. We first show that, due to the characteristic of sampling efficiency in Monte Carlo simulation, granularity of samples is an important issue in achieving high overall efficiency. The proposed techniques perform sampling both temporally (across differ...

Improving phylogenetic analyses by incorporating additional information from genetic sequence databases

MOTIVATION Statistical analyses of phylogenetic data culminate in uncertain estimates of underlying model parameters. Lack of additional data hinders the ability to reduce this uncertainty, as the original phylogenetic dataset is often complete, containing the entire gene or genome information available for the given set of taxa. Informative priors in a Bayesian analysis can reduce posterior un...

A survey of Monte Carlo algorithms for maximizing the likelihood of a two-stage hierarchical model

Hierarchical Bayesian Modeling of Human Decision-Making Using Wiener Diffusion

Wiener diffusion accounts of human decision-making are among the most successful and best developed formal models in the psychological sciences. We reconsider these models from a Bayesian perspective, using graphical modeling, and Markov Chain Monte-Carlo methods for posterior sampling. By analyzing seminal data from a brightness discrimination task, we show how the Bayesian approach offers sev...