Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 17 of 17 for “"Computational Statistics"”.
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Applications of computational statistics in cognitive diagnosis and IRT modeling
The identifiability and estimability of the parameters for the Unified Cognitive/IRT Model are studies. A calibration procedure for the Unified Model is then proposed. This procedure uses the marginal maximum likelihood estimation approach and utilizes the EM algorithm. It differs from other …
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Contributions to statistical machine learning algorithm
This thesis's research focus is on computational statistics along with DEAR (abbreviation of differential equation associated regression) model direction, and that in mind, the journal papers are written as contributions to statistical machine learning algorithm literature.
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Efficient Sampling Methods of, by, and for Stochastic Dynamical Systems
… methodologies that lie at the intersection of computational statistics and computational dynamics. Stochastic differential equations (SDEs) are used to model a variety of physical systems, and computing expectations over marginal distributions of SDEs is important for the analysis of such …
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Three Essays on High-Frequency and High-Dimensional Financial Data Analysis
… of asset pricing, financial econometrics, and computational statistics using large-scale financial data techniques. In terms of asset pricing (Chapter 2), I investigate the relationship between the cross-section of expected stock returns and the associated market risks. In terms of financial …
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Theoretical Foundations of Flow-based Methods for Sampling and Generative Modeling
… probability distribution is a central problem in computational statistics and machine learning. Transportation of measure offers a useful approach to this problem: the idea is to construct a measurable map that pushes forward a relatively simple source distribution to the target probability …
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Statistical algorithms using multisets and statistical inference of heterogeneous networks
Computational statistics, including methods such as Markov chain Monte Carlo (MCMC), bootstrap, approximate Bayesian computation, is an important part in modern statistics and has been widely used in many areas, such as Bayesian statistics, computational biology, and computational physics. In this …
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Modularized Bayesian Inference: Methodology, Algorithm, Theory And Application.
… is proposed in this thesis. The theoretical and computational properties of the SACut algorithm are studied. A general framework of cut inference beyond a generic two-module case, where one component is assumed to be misspecified, is not clear. In particular, the definition of what a ``module'' …
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Exploring Probability Measures with Markov Processes
… their invariant measure. These distinctions pose computational and theoretical challenges for the design, analysis, and implementation of PDMP-based samplers. The key contribution of this work is to develop a transparent characterisation of how one can construct a PDMP (within the class of …
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Natural gradient methods in statistics and machine learning
… to converge using standard methods, making them computationally demanding. In this thesis, we present methods for performing such optimisations efficiently, drawing heavily on the use of natural gradient methods (Amari, 1998). In the first main contribution chapter of this thesis, we present a …
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Spatial-Statistical Properties of Geochemical Variability as Constraints on Magma Transport and Evolution Processes at Ocean Ridges
… I apply techniques of exploratory data analysis, computational statistics, and petrologic modeling to develop original ideas about the relationship between sampled major element variability and the effects of specific processes, both petrogenetic and scientific: crystallization, melt transport, …
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Inverse Problems in Agent-Based Models of Spatial Phenomena
… (agent) behaviours from aggregate summary statistics constitutes a fundamental inverse problem in the study of socio-physical systems. This thesis examines a key facet of this problem that involves inferring the discrete origin-destination matrix (ODM) of agent trip counts between spatial …
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Bayesian model-based clustering of multi-source data
Inferring a partition of a dataset can help in downstream analyses and decision making. However, there often exist many feasible partitions, which makes the problem of inferring clusters challenging. A demanding problem is analysis of data generated across multiple sources. Bayesian mixture models …
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Bayesian approaches to time-frequency inverse problems
… of audio inverse problems. Chapter 5 proposes a computationally feasible expectation–maximisation scheme to compute the latent parameters of the LRTFS model and examines its application in simultaneous reconstruction and source separation tasks. In turn, Chapter 6 focuses on the reconstruction of …
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PRACTICAL INVESTIGATIONS ON BAYESIAN INVERSE PROBLEMS
… which are asymptotically justified. As the computational power increased, however, ractitioners and researchers looked for better uncertainty quantification. The usual asymptotic confidence intervals gave way to full distributions using the Bayesian approach. This approach to inverse …
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Essays on Model Selection Uncertainty and Model Averaging: Computational and Empirical Work with Beta Regression, Multiple Linear Regression with ARMA Innovations, and the Minimum Description Length Principle
… generalizability, practical usefulness, and computational ease. This is problematic as model selection routinely admits multiple models which imposes extra uncertainty on all post-selection conclusions. This research emphasized integrated model averaging and selection methods to enhance the …
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Bayesian Time Series Learning with Gaussian Processes
… Its main advantage is that it avoids the computationally expensive (and potentially difficult to tune) smoothing step that is a key part of learning nonlinear state-space models.
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Physical modelling of galaxy clusters and Bayesian inference in astrophysics
This thesis is concerned with the modelling of galaxy clusters, applying these models to real and simulated data using Bayesian inference, and the development of Bayesian inference algorithms applicable to a wide range of astrophysical problems. I present a comparison of mass estimates for $54$ …