Browsing by Subject "MCMC"
Now showing items 1-8 of 8
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A Bayesian approach for individual-level drug benefit-risk assessment.
(Statistics in medicine, 2019-07)In existing benefit-risk assessment (BRA) methods, benefit and risk criteria are usually identified and defined separately based on aggregated clinical data and therefore ignore the individual-level differences as well as ... -
A Bayesian Hierarchical Model with SNP-level Functional Priors Applied to a Pathway-wide Association Study.
(2010)Tremendous effort has been put into study of the etiology of complexdiseases including the breast cancer, type 2 diabetes,cardiovascular diseases, and prostate cancers. Despite large numbers of reported disease-associated ... -
Bayesian Computation for High-Dimensional Continuous & Sparse Count Data
(2018)Probabilistic modeling of multidimensional data is a common problem in practice. When the data is continuous, one common approach is to suppose that the observed data are close to a lower-dimensional smooth manifold. There ... -
Bayesian Models for Combining Information from Multiple Sources
(2022)This dissertation develops Bayesian methods for combining information from multiple sources. I focus on developing Bayesian bipartite modeling for simultaneous regression and record linkage, as well as leveraging auxiliary ... -
Comparison of Bayesian Inference Methods for Probit Network Models
(2021)This thesis explores and compares Bayesian inference procedures for probit network models. Network data typically exhibit high dyadic correlation due to reciprocity. For binary network data, presence of dyadic correlation ... -
MCMC Sampling Geospatial Partitions for Linear Models
(2021)Geospatial statistical approaches must frequently confront the problem of correctlypartitioning a group of geographical sub-units, such as counties, states, or precincts,into larger blocks which share information. Since ... -
Modeling Time-Varying Networks with Applications to Neural Flow and Genetic Regulation
(2010)Many biological processes are effectively modeled as networks, but a frequent assumption is that these networks do not change during data collection. However, that assumption does not hold for many phenomena, such as neural ... -
Stratified MCMC Sampling of non-Reversible Dynamics
(2020)The study of stratified sampling is of interest in systems which canbe solved accurately on small scales, or which depend heavily on rare transitions of particles from one subspace to another. We present a new form of stratified ...
