Eliciting information from experts for use in constructing prior distributions for logistic regression coefficients can be challenging. The task is especially difficult when the model contains many predictor variables, because the expert is asked to provide summary information about the probability of “success” for many subgroups of the population. Often,...
This paper examines the regulatory decision problem facing fishery managers tasked with balancing desirable fish catch with undesirable bycatch. I approach the problem using Bayesian decision analysis. I first estimate a model of production, accounting for the fishery’s joint production of desirable and undesirable outputs. I then use the estimated...
This study investigated variation in xylem anatomy, hydraulic properties, and the relationship between anatomy and properties within Douglas-fir trees at multiple scales. The hierarchical scales in the study included fertilization treatments (fertilized and unfertilized), trees within the treatments, and positions within the trees. Tracheid diameter, tracheid length, percent latewood, number...
This study investigated variation in xylem anatomy, hydraulic properties, and the relationship between anatomy and properties within Douglas-fir trees at multiple scales. The hierarchical scales in the study included fertilization treatments (fertilized and unfertilized), trees within the treatments, and positions within the trees. Tracheid diameter, tracheid length, percent latewood, number...
A dynamic approach to the Bayesian theory of sampling inspection
by attributes for the single sampling case is presented. A model is
developed which assumes a sequence of lots of equal known size and
lots generated by a process operating in a random manner. The model
also assumes constant associated...
Motivation: The goal of any parentage analysis is to identify as many parent-offspring relationships as possible, while minimizing incorrect assignments. Existing methods can achieve these ends, but require additional information in the form of demographic data, thousands of markers, and/or estimates of genotyping error rates. For many non-model systems, it...
A Bayesian approach to the analysis of a two-phase linear
regression model is given. It is assumed that the regression model is
continuous at the change point. The likelihood function is expressed
in a form which explicitly contains the continuity restriction. The
natural conjugate prior distribution for the likelihood function...
This dissertation explores and analyzes the performance of several Bayesian anytime inference algorithms for dynamic influence diagrams. These algorithms are compared on the On-Line Maintenance Agent testbed, a software artifact permitting comparison of dynamic reasoning algorithms used by an agent on a variety of simulated maintenance and monitoring tasks. Analysis...
Bayesian inferential methods for the two parameter Weibull (and
extreme-value distribution) are presented in a life-testing context. A
practical method of calculating posterior distributions of the two parameters
and a large class of functions of the parameters is presented.
The emphasis is for the situation where the sample information is...
Over the past decade, it has come to light that many published scientific findings cannot be reproduced. This has led to the replication crisis in science. Many researchers feel that they can no longer trust much of what they read in scientific journals, and the public is becoming ever more...
Probabilistic inference using Bayesian networks is now a well-established approach for reasoning under uncertainty. Among many e ciency-driven tech- niques which have been developed, the Optimal Factoring Problem (OFP) is distinguished for presenting a combinatorial optimization point of view on the problem. The contribution of this thesis is to extend...
An important challenge in machine learning is to find ways of learning quickly from very small amounts of training data. The only way to learn from small data samples is to constrain the learning process by exploiting background knowledge. In this report, we present a theoretical analysis on the use...
Bayesian Optimization (BO) methods are often used to optimize an unknown function f(•) that is costly to evaluate. They typically work in an iterative manner. In each iteration, given a set of observation points, BO algorithms select k ≥ 1 points to be evaluated. The results of those points are...
Finite order autoregressive models for time series are often
used for prediction and other inferences. Given the order of the
model, the parameters of the models can be estimated by least
squares, maximum likelihood, or the Yule-Walker method. The
basic problem is estimating the order of the model. A number...
Image segmentation continues to be a fundamental problem in computer vision and image understanding. In this thesis, we present a Bayesian network that we use for object boundary detection in which the MPE (most probable explanation) before any evidence can produce multiple non-overlapping, non-self-intersecting closed contours and the MPE with...