An attempt is made to solve two classes of nonlinear time series prediction problems with a hierarchical Bayes Approach using neural nets.

In experimental sciences many classification problems deal with variables with replicated measurements. In this case the replicates are usually summarized by their mean or median. However, such choice does not consider the information about the uncertainty associated with the measurements, thus potentially leading to over or underestimate the probability associated to each classification. In th...

Abstract Judging by its significant potential to affect the outcome of a game in one single action, penalty kick is arguably most important set piece football. Scientific studies on how ability convert distributed among professional football players are scarce. In this paper, we consider rank takers German Bundesliga based historical data from 1963 2021. We use Bayesian models that improve infe...

Optical proximity correction (OPC) is one of the most important techniques in today’s optical lithography-based manufacturing process. Although the most widely used model-based OPC is expected to achieve highly accurate correction, it is also known to be extremely time-consuming. This paper proposes a regression model for OPC using a hierarchical Bayes model (HBM). The goal of the regression mo...

Hierarchical Bayes models free researchers from computational constraints and allow researchers and practitioners to develop more realistic models of buyer behavior and decision making. Moreover, this freedom enables exploration of marketing problems that have proven elusive over the years, such as models for advertising ROI, sales force effectiveness, and similarly complex problems that often ...

We present a novel method for learning with Gaussian process regression in a hierarchical Bayesian framework. In a first step, kernel matrices on a fixed set of input points are learned from data using a simple and efficient EM algorithm. This step is nonparametric, in that it does not require a parametric form of covariance function. In a second step, kernel functions are fitted to approximate...

Differences in treatment effects between centers in a multi-center trial may be important. These differences represent treatment by subgroup interaction. Peto defines qualitative interaction (QI) to occur when the simple treatment effect in one subgroup has a different sign than in another subgroup: this interaction is important. Interaction where the treatment effects are of the same sign in a...

MELISSA GROUT SMITH. Robust Hierarchical Bayes Methodology for Clinical Studies (Under the joint direction of Drs. Clarence E. Davis and Richard 1. Smith.) Outlier observations can have an adverse effect on statistical inference. Identification and elimination of such observations are one option, however, dealing with outliers in this manner has many drawbacks. An alternative approach is to uti...