Browsing by Subject "Gaussian process"
Now showing items 1-16 of 16
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Accommodating the ecological fallacy in disease mapping in the absence of individual exposures.
(Stat Med, 2017-09-19)In health exposure modeling, in particular, disease mapping, the ecological fallacy arises because the relationship between aggregated disease incidence on areal units and average exposure on those units differs from the ... -
Bayesian Nonparametric Modeling and Theory for Complex Data
(2012)The dissertation focuses on solving some important theoretical and methodological problems associated with Bayesian modeling of infinite dimensional `objects', popularly called nonparametric Bayes. The term `infinite dimensional ... -
Computational Methods for Investigating Dendritic Cell Biology
(2011)The immune system is constantly faced with the daunting task of protecting the host from a large number of ever-evolving pathogens. In vertebrates, the immune response results from the interplay of two cellular systems: ... -
Data augmentation for models based on rejection sampling.
(Biometrika, 2016-06)We present a data augmentation scheme to perform Markov chain Monte Carlo inference for models where data generation involves a rejection sampling algorithm. Our idea is a simple scheme to instantiate the rejected proposals ... -
Development and Implementation of Bayesian Computer Model Emulators
(2011)Our interest is the risk assessment of rare natural hazards, such aslarge volcanic pyroclastic flows. Since catastrophic consequences ofvolcanic flows are rare events, our analysis benefits from the use ofa computer model ... -
Efficient Bayesian Nonparametric Methods for Model-Free Reinforcement Learning in Centralized and Decentralized Sequential Environments
(2014)As a growing number of agents are deployed in complex environments for scientific research and human well-being, there are increasing demands for designing efficient learning algorithms for these agents to improve their ... -
Efficient Gaussian process regression for large datasets.
(Biometrika, 2013-03)Gaussian processes are widely used in nonparametric regression, classification and spatiotemporal modelling, facilitated in part by a rich literature on their theoretical properties. However, one of their practical limitations ... -
Kernel Averaged Predictors for Space and Space-Time Processes
(2011)In many spatio-temporal applications a vector of covariates is measured alongside a spatio-temporal response. In such cases, the purpose of the statistical model is to quantify the change, in expectation or otherwise, in ... -
Multivariate Spatial Process Gradients with Environmental Applications
(2014)Previous papers have elaborated formal gradient analysis for spatial processes, focusing on the distribution theory for directional derivatives associated with a response variable assumed to follow a Gaussian process model. ... -
Nonlinear Prediction in Credit Forecasting and Cloud Computing Deployment Optimization
(2015)This thesis presents data analysis and methodology for two prediction problems. The first problem is forecasting midlife credit ratings from personality information collected during early adulthood. The second problem is ... -
Numerical method for parameter inference of systems of nonlinear ordinary differential equations with partial observations.
(Royal Society open science, 2021-07-28)Parameter inference of dynamical systems is a challenging task faced by many researchers and practitioners across various fields. In many applications, it is common that only limited variables are observable. In this paper, ... -
Predictive Models for Point Processes
(2015)Point process data are commonly observed in fields like healthcare and social science. Designing predictive models for such event streams is an under-explored problem, due to often scarce training data. In this thesis, a ... -
Scalable Nonparametric Bayes Learning
(2013)Capturing high dimensional complex ensembles of data is becoming commonplace in a variety of application areas. Some examples includebiological studies exploring relationships between genetic mutations and diseases, atmospheric ... -
Sensor Planning for Bayesian Nonparametric Target Modeling
(2016)Bayesian nonparametric models, such as the Gaussian process and the Dirichlet process, have been extensively applied for target kinematics modeling in various applications including environmental monitoring, traffic planning, ... -
Statistical Analysis of Response Distribution for Dependent Data via Joint Quantile Regression
(2021)Linear quantile regression is a powerful tool to investigate how predictors may affect a response heterogeneously across different quantile levels. Unfortunately, existing approaches find it extremely difficult to adjust ... -
Topics in Bayesian Spatiotemporal Prediction of Environmental Exposure
(2019)We address predictive modeling for spatial and spatiotemporal modeling in a variety of settings. First, we discuss spatial and spatiotemporal data and corresponding model types used in later chapters. Specifically, we discuss ...
