Thesis

Bayesian methods in bidding processes

January 2003

Thesis for: PhD

Authors:

King Juan Carlos University

Modeling External Risks in Project Management

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Sep 2007

To ascertain the viability of a project, undertake resource allocation, take part in bidding processes, and other related decisions, modern project management requires forecasting techniques for cost, duration, and performance of a project, not only under normal circumstances, but also under external events that might abruptly change the status quo. We provide a Bayesian framework that provides a global forecast of a project's performance. We aim at predicting the probabilities and impacts of a set of potential scenarios caused by combinations of disruptive events, and using this information to deal with project management issues. To introduce the methodology, we focus on a project's cost, but the ideas equally apply to project duration or performance forecasting. We illustrate our approach with an example based on a real case study involving estimation of the uncertainty in project cost while bidding for a contract.

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Improved Determination of the Myelin Water Fraction in Human Brain using Magnetic Resonance Imaging...

October 2015 · NeuroImage

Myelin water fraction (MWF) mapping with magnetic resonance imaging has led to the ability to directly observe myelination and demyelination in both the developing brain and in disease. Multicomponent driven equilibrium single pulse observation of T1 and T2 (mcDESPOT) has been proposed as a rapid approach for multicomponent relaxometry and has been applied to map MWF in human brain. However, even ... [Show full abstract] for the simplest two-pool signal model consisting of MWF and non-myelin-associated water, the dimensionality of the parameter space for obtaining MWF estimates remains high. This renders parameter estimation difficult, especially at low-to-moderate signal-to-noise ratios (SNR), due to the presence of local minima and the flatness of the fit residual energy surface used for parameter determination using conventional nonlinear least squares (NLLS)-based algorithms. In this study, we introduce three Bayesian approaches for analysis of the mcDESPOT signal model to determine MWF. Given the high dimensional nature of mcDESPOT signal model, and, thereby, the high dimensional marginalizations over nuisance parameters needed to derive the posterior probability distribution of MWF parameter, the introduced Bayesian analyses use different approaches to reduce the dimensionality of the parameter space. The first approach uses normalization by average signal amplitude, and assumes that noise can be accurately estimated from signal-free regions of the image. The second approach likewise uses average amplitude normalization, but incorporates a full treatment of noise as an unknown variable through marginalization. The third approach does not use amplitude normalization and incorporates marginalization over both noise and signal amplitude. Through extensive Monte Carlo numerical simulations and analysis of in-vivo human brain datasets exhibiting a range of SNR and spatial resolution, we demonstrated the markedly improved accuracy and precision in the estimation of MWF using these Bayesian methods as compared to the stochastic region contraction (SRC) implementation of NLLS. Key words: mcDESPOT; Myelin water fraction; Bayesian analysis; Stochastic region contraction algorithm; Nonlinear least squares; Brain.

Book

Maximum-Entropy and Bayesian Spectral Analysis and Estimation Problems: Proceedings of the Third Wor...

January 1987

This volume has its origin in the third ·Workshop on Maximum-Entropy and Bayesian Methods in Applied Statistics,· held at the University of Wyoming, August 1 to 4, 1983. It was anticipated that the proceedings of this workshop could not be prepared in a timely fashion, so most of the papers were not collected until a year or so ago. Because most of the papers are in the nature of advancing theory ... [Show full abstract] or solving specific problems, as opposed to status reports, it is believed that the contents of this volume will be of lasting interest to the Bayesian community. The workshop was organized to bring together researchers from differ ent fields to examine critically maximum-entropy and Bayesian methods in science, engineering, medicine, economics, and other disciplines. Some of the papers were chosen specifically to kindle interest in new areas that may offer new tools or insight to the reader or to stimulate work on pressing problems that appear to be ideally suited to the maximum-entropy or Bayes ian method.

Article

A Bayesian Approach to the Multiple Comparisons Problem

January 2006 · Journal of data science: JDS

Consider the problem of selecting independent samples from several populations for the purpose of between-group comparisons. An im-portant aspect of the solution is the determination of clusters where mean levels are equal, often accomplished using multiple comparisons testing. We formulate the hypothesis testing problem of determining equal-mean clus-ters as a model selection problem. ... [Show full abstract] Information from all competing models is combined through Bayesian methods in an effort to provide a more re-alistic accounting of uncertainty. An example illustrates how the Bayesian approach leads to a logically sound presentation of multiple comparison re-sults.

Article

Bayesian analysis of conditional density functions: a limited information approach

January 1970

Jungmo Yoon

We propose a flexible Bayesian method for conditional density function es-timation and prove its posterior consistency. Its usefulness is illustrated by estimating predictive densities of multi-factor diffusion processes which has many applications in as-set pricing. To model the unknown conditional density, we use location mixtures where the mixing distribution of location parameters plays an ... [Show full abstract] important role for inference. The key idea of this paper is to use conditional quantiles as a mixing distribution. We con-struct the posterior distribution on the space of mixing distribution, i.e. on the space of conditional quantiles, and derive the posterior on the conditional density. A fully non-parametric method for conditional density or distribution function estimation may suffer from the curse of dimensionality. Our method provides a dimension reduction strategy by imposing reasonable structures on conditional quantiles. Its virtue is better finite sample performance. In our simulation exercises where we estimate predictive densities of diffusion processes we find that our Bayes estimator performs better than the well-known double kernel method.

Article

Distinguishing Normative Processes From Noise: A Comparison of Four Approaches to Modeling Impressio...

November 2016 · Social Psychology Quarterly

This research evaluates the relative merits of two established and two newly proposed methods for modeling impressions of social events: stepwise regression, ANOVA, Bayesian model averaging, and Bayesian model sampling. Models generated with each method are compared against a ground truth model to assess performance at variable selection and coefficient estimation. We also assess the theoretical ... [Show full abstract] impacts of different modeling choices. Results show that the ANOVA procedure has a significantly lower false discovery rate than stepwise regression, whereas Bayesian methods exhibit higher true positive rates and comparable false discovery rates to ANOVA. Bayesian methods also generate coefficient estimates with less bias and variance than either stepwise regression or ANOVA. We recommend the use of Bayesian methods for model specification in affect control theory.

Article

Full-text available

Bayesian prediction of peak resistance of a spudcan penetrating sand-over-clay

January 2018 · Geotechnique

Assessing the potential for a punch-through failure during spudcan installation in sand-over-clay is crucial for reducing risk in the operations of mobile jack-up platforms. Typically, in the offshore industry, the peak penetration resistance and the depth at which it occurs are determined deterministically without rigorously considering the uncertainties in the soil. This paper proposes a ... [Show full abstract] probabilistic approach to estimate the peak resistance and the corresponding depth, as well as a Bayesian method of incorporating installation data to update the predictions. Instead of a single value in the deterministic analysis, a range of the potential peak resistances and depths can be estimated by accounting for the uncertainties in the soil, the spudcan's geometry and in the calculation method itself, with a database of 66 geotechnical centrifuge tests characterising the model. This prior probability is then updated using the monitored data, allowing a real-time update of the probabilities associated with candidate values of peak resistance and depth during the installation. The advantage of such a probabilistic updating model is shown in a retrospective simulation of a mobile jack-up platform in sand-over-clay conditions in the Gulf of Mexico. The results show that the prior estimation can be effectively refined by incorporating the monitored data. The proposed method provides a powerful tool for assisting decision-making during the installation of jack-ups offshore.

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Comparing Phylogenetic Codivergence between Polyomaviruses and Their Hosts

January 2006 · Journal of Virology

Seventy-two full genomes corresponding to nine mammalian (67 strains) and two avian (5 strains) polyomavirus species were analyzed using maximum likelihood and Bayesian methods of phylogenetic inference. Our fully resolved and well-supported (bootstrap proportions > 90%; posterior probabilities = 1.0) trees separate the bird polyomaviruses (avian polyomavirus and goose hemorrhagic polyomavirus) ... [Show full abstract] from the mammalian polyomaviruses, which supports the idea of spitting the genus into two subgenera. Such a split is also consistent with the different viral life strategies of each group. Simian (simian virus 40, simian agent 12 [Sa12], and lymphotropic polyomavirus) and rodent (hamster polyomavirus, mouse polyomavirus, and murine pneumotropic polyomavirus [MPtV]) polyomaviruses did not form monophyletic groups. Using our best hypothesis of polyomavirus evolutionary relationships and established host phylogenies, we performed a cophylogenetic reconciliation analysis of codivergence. Our analyses generated six optimal cophylogenetic scenarios of coevolution, including 12 codivergence events (P < 0.01), suggesting that Polyomaviridae coevolved with their avian and mammal hosts. As individual lineages, our analyses showed evidence of host switching in four terminal branches leading to MPtV, bovine polyomavirus, Sa12, and BK virus, suggesting a combination of vertical and horizontal transfer in the evolutionary history of the polyomaviruses.

Article

Bayesian and profile likelihood change point methods for modeling cognitive function over time

February 2003 · Computational Statistics & Data Analysis

Change point models are often used to model longitudinal data. To estimate the change point, Bayesian (Biometrika 62 (1975) 407; Appl. Statist. 41 (1992) 389; Biometrics 51 (1995) 236) or profile likelihood (Statist. Med. 19 (2000) 1555) methods may be used.We compare and contrast the two methods in analyzing longitudinal cognitive data from the Bronx Aging Study. The Bayesian method has ... [Show full abstract] advantages over the profile likelihood method in that it does not require all subjects to have the same change point. Caution must be taken regarding sensitivity to choice of prior distribution, identifiability, and goodness of fit. Analyses show that decline in memory precedes diagnosis of dementia by 7.5–8 years, and individual change points are not needed to model heterogeneity across subjects.

Article

Bayesian analysis of recognition memory: The case of the list-length effect

October 2008 · Journal of Memory and Language

Recognition memory experiments are an important source of empirical constraints for theories of memory. Unfortunately, standard methods for analyzing recognition memory data have problems that are often severe enough to prevent clear answers being obtained. A key example is whether longer lists lead to poorer recognition performance. The presence or absence of such a list-length effect is a ... [Show full abstract] critical test of competing item- and context-noise based theories of interference and bares on whether recognition involves “recall-like” components as dual process theories would contend. However, the issue has remained unresolved, in part, because of the weaknesses of the standard analysis. In this paper, we develop a Bayesian method of analysis and apply it to new data on the list-length effect. The analysis allows us to find positive evidence in favor of a null list-length effect as predicted by context noise models. The data also illustrate the importance of the contextual reinstatement process on recognition performance and show how previous work demonstrating a list-length effect may have been contaminated by reinstatement confounds. By contrasting our new method against the standard approach we highlight the advantages of the Bayesian framework when inferring the values of psychologically meaningful variables, and in choosing between models representing different theoretical assumptions about memory.

Article

BELIEFS AND VALUES IN AGRICULTURAL ECONOMICS RESEARCH

February 1985 · Australian Journal of Agricultural and Resource Economics

J. Brian Hardaker

Attention is drawn to the values that are often kept hidden but condition what research agricultural economists do. It is pointed out that the economic theory, which guides most research, is not value free and that therefore the lack of debate about theory amongst Australian agricultural economists may be unhealthy. In a world with uncertainty, the research process is likened to learning, leading ... [Show full abstract] to a suggestion that Bayesian procedures provide a more satisfactory conceptual framework for the process of enquiry than does the conventional hypothesistesting approach. While difficulties in the practical applicability of Bayesian methods are understood, they do provide insights which permit some of the more inappropriate uses of hypothesis testing to be recognised. The view of research enquiry as learning also leads to an appreciation of the desirability of a more honest acceptance of the inherent subjectivity of the process. Finally, attention is drawn to the need for vigilance over professional standards in research.

Article

Bayesian estimation of exchange rate regime choice with spatial effect

December 2012 · Economics Letters

Guoxiong Zhang

Exchange rate arrangements and trade are inherently connected. Exchange rate volatility has a significant impact on trade volumes, and trading partners thus could affect each other’s exchange rate regime choice. This spatial effect among trading partners has been overlooked in empirical studies of countries’ exchange rate regime choices. This paper addresses this deficit by presenting a ... [Show full abstract] specifically designed ordered probit model with spatial effect. The model is estimated using a Bayesian method that utilizes data augmentation with Gibbs sampling. The results obtained here differ markedly from those presented in the existing literature.

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Application of classification methods when group sizes are unequal by incorporation of prior probabi...

December 2009 · Chemometrics and Intelligent Laboratory Systems

Four common classification methods are described, Euclidean Distance to Centroids (EDC), Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA) and Support Vector Machines (SVM). In many applications of chemometrics e.g. in medicine and biology it is common for there to be unequal sample sizes in different groups. When class sizes are unequal the performance of some of these ... [Show full abstract] methods may be biased according to class size. This paper describes approaches for incorporating prior probabilities of class membership using Bayesian approaches to three of the methods LDA, QDA and SVM, either assuming equal probability or assuming that the relative sample sizes relate to the relative probabilities. EDC is used as a benchmark to determine model stabilities. The methods are illustrated by four simulated datasets of different structures and one real dataset consisting of the gas chromatographic profile of mouse urine comparing controls to those on a diet.