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Bayesian forecasting of motor recovery following cortical infarcts
(201512)Globally, about 15 million people suffer a stroke each year. Of these affected, about 5 million die and another 6 million are left with longterm disability. The cause of this disability is often due to motor, or muscle, ... 
Bayesian forecasting of Prepayment Rates for Individual Pools of Mortgages
(2008)This paper proposes a novel approach for modeling prepayment rates of individual pools of mortgages. The model incorporates the empirical evidence that prepayment is past dependent via Bayesian methodology. There are many ... 
Bayesian hierarchical linear modeling of NFL quarterback rating
(201505)With endless amounts of statistics in American football, there are numerous ways to evaluate quarterback performance in the National Football League. Owners, general managers, and coaches are always looking for ways to ... 
Bayesian hierarchical modelling of pavement performance
(201505)A challenge currently faced by local, state and federal transportation agencies is the constantly increasing traffic demand, combined with a less increasing availability of funds for the maintenance of the highway ... 
Bayesian hierarchical parametric survival analysis for NBA career longevity
(201205)In evaluating a prospective NBA player, one might consider past performance in the player’s previous years of competition. In doing so, a general manager may ask the following questions: Do certain characteristics of a ... 
Bayesian inference for random partitions
(201308)I consider statistical inference for clustering, that is the arrangement of experimental units in homogeneous groups. In particular, I discuss clustering for multivariate binary outcomes. Binary data is not very informative, ... 
Bayesian inference methods for next generation DNA sequencing
(201408)Recently developed nextgeneration sequencing systems are capable of rapid and costeffective DNA sequencing, thus enabling routine sequencing tasks and taking us one step closer to personalized medicine. To provide a ... 
Bayesian learning methods for neural coding
(201312)A primary goal in systems neuroscience is to understand how neural spike responses encode information about the external world. A popular approach to this problem is to build an explicit probabilistic model that characterizes ... 
Bayesian learning methods for potential energy parameter inference in coarsegrained models of atomistic systems
(201505)The present work addresses issues related to the derivation of reduced models of atomistic systems, their statistical calibration, and their relation to atomistic models of materials. The reduced model, known in the chemical ... 
Bayesian learning with catastrophe risk : information externalities in a large economy
(201108)Based on a previous study by Amador and Weill (2009), I study the diffusion of dispersed private information in a large economy subject to a ”catastrophe risk” state. I assume that agents learn from the actions of oth ers ... 
Bayesian Logic Programs for plan recognition and machine reading
(201212)Several real world tasks involve data that is uncertain and relational in nature. Traditional approaches like firstorder logic and probabilistic models either deal with structured data or uncertainty, but not both. To ... 
Bayesian mediation analysis for partially clustered designs
(201305)Partially clustered design is common in medicine, social sciences, intervention and psychological research. With some participants clustered and others not, the structure of partially clustering data is not parallel. Despite ... 
Bayesian Mixture Models for Assessment of Gene Differential Behaviour and Prediction of pCR through the Integration of Copy Number and Gene Expression Data
(PLOS One, 20130712)We consider modeling jointly microarray RNA expression and DNA copy number data. We propose Bayesian mixture models that define latent Gaussian probit scores for the DNA and RNA, and integrate between the two platforms via ... 
A Bayesian network classifier for quantifying design and performance flexibility with application to a hierarchical metamaterial design problem
(201312)Design problems in engineering are typically complex, and are therefore decomposed into a hierarchy of smaller, simpler design problems by the design management. It is often the case in a hierarchical design problem that ... 
Bayesian network classifiers for setbased collaborative design
(201012)For many products, the design process is a complex system involving the interaction of many distributed design activities that need to be carefully coordinated. This research develops a new tool, called a Bayesian network ... 
Bayesian passive sonar tracking in the context of activepassive data fusion
(200908)This thesis investigates the improvements that can be made to Bayesian passive sonar tracking in the context of activepassive sonar data fusion. Performance improvements are achieved by exploiting the prior information ... 
Bayesian ridge estimation of ageperiodcohort models
(201408)AgePeriodCohort models offer a useful framework to study trends of timespecific phenomena in various areas. Yet the perfect linear relationship among age, period, and cohort induces a singular design matrix and brings ... 
Bayesian variable selection for GLM
(2002) 
Bayesians Can Learn From Old Data
(200711)In a widelycited paper, Glymour (Theory and Evidence, Princeton, N. J.: Princeton University Press, 1980, pp. 6393) claims to show that Bayesians cannot team from old data. His argument contains an elementary error. I ...