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 20 of 184 for “"probabilistic models"”.
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Guiding Deep Probabilistic Models
Deep probabilistic models utilize deep neural networks to learn probability distributions in high-dimensional data spaces. Learning and inference in these models are complicated due to the difficulty of direct evaluation of the differences between the model distribution and the target. This thesis …
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Probabilistic models for drug dissolution
… stochastic Direct and Inverse Monte-Carlo-based models for drug dissolution. Drug dissolution from different carriers is a complex phenomenon. Limited knowledge is available on some of the underlying constituent processes, which restricts development of mechanistic models. Monte Carlo techniques …
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Probabilistic Models on Fibre Bundles
<p>In this thesis, we propose probabilistic models on fibre bundles for learning the generative process of data. The main tool we use is the diffusion kernel and we use it in two ways. First, we build from the diffusion kernel on a fibre bundle a projected kernel that generates robust …
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Probabilistic Models For Population Dynamics
Two interacting particle systems that serve as probabilistic models for population dynamics are studied in this work. The quadratic contact process is a stochastic spatial model for a population in which each individual has two parents and the dynamics are governed by random birth and death rates …
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Advances in Compression using Probabilistic Models
… data. One emerging solution lies in applying probabilistic machine learning to capture the data distribution in an unsupervised manner. Once a probabilistic model for the data is defined, variational inference can be used to infer its parameters from data. Variational inference is closely …
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Generative probabilistic models of neuron morphology
… in large networks to meaningful null models. However, those goals raise significant computational challenges. In particular, since neural morphology spans six orders of magnitude in length (roughly 1 nm-1 mm), a spatial hierarchy of representations is needed to capture micron-scale …
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Probabilistic models for multi-relational data analysis.
… on a single source of data alone. We develop probabilistic models for multi-relational data analysis due to their advantage in incorporating prior knowledge from multiple sources through prior distributions, and their modularity in combining multiple models through sharing latent variables. By …
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Probabilistic models for mobile phone trajectory estimation
… dissertation presents three systems, all using probabilistic models, to accomplish this matching. The first, VTrack, uses Hidden Markov Models to match noisy or sparsely sampled geographic (lat, lon) coordinates to a sequence of road segments on a map. We evaluate VTrack on 800 drive hours of …
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Performance Analysis of Cyber Deception Using Probabilistic Models
… honeypots. This work goes on to analyze these models in two scenarios, gaining a foothold and minimum to win, providing insight into the effect both defenses can have under various environments. Finally, this thesis performs an empirical analysis of network address shuffling to provide a …
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Monte Carlo integration in discrete undirected probabilistic models
… of Monte Carlo sampling for undirected graphical models, a class of statistical model commonly used in machine learning, computer vision, and spatial statistics; the aim is to be able to use the methodology and resultant samples to estimate integrals of functions of the variables in the model. …
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A Framework for Combining Logical and Probabilistic Models
… expressive power of first-order logic with the probabilistic reasoning power of Bayesian networks has attracted the interest of many researchers. We review many techniques for integration of first-order logic and Bayesian networks and propose a new framework that exploits the translation of …
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Probabilistic Models and Algorithmic Analysis of Network Problems
… of graph mining. We approach these problems by probabilistic tools, to model, analyze, and design algorithms. In the first problem, we aim to improve upon the known bounds of some fundamental distributed algorithms, Minimum Spanning Tree (MST) in particular. We propose the Smoothed Analysis, …
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Probabilistic Models for Human Migration Forecasting and Residency Imputation
I develop probabilistic models to enhance the estimation and forecasting of human migration flows and residency. Using a Bayesian hierarchical approach, I first propose a model for forecasting global bilateral migration flows among the 200 most populous countries, producing well-calibrated …
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Beyong lexical meaning : probabilistic models for sign language recognition
This thesis presents a probabilistic framework for recognizing multiple simultaneously expressed concepts in sign language gestures. These gestures communicate not just the lexical meaning but also grammatical information, i.e. inflections that are expressed through systematic spatial and temporal …
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Probabilistic models to resolve cell identity and tissue architecture
… TF activities. In my thesis, I developed two probabilistic models that advance the understanding of these processes using single-cell and spatial genomic data. Spatial transcriptomic technologies promise to resolve cellular wiring diagrams of tissues in health and disease, but comprehensive …
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Separability as a modeling paradigm in large probabilistic models
Many interesting stochastic models can be formulated as finite-state vector Markov processes, with a state characterized by the values of a collection of random variables. In general, such models suffer from the curse of dimensionality: the size of the state space grows exponentially with the …
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Rare event simulation for probabilistic models of T-cell activation
… by several antigens. The usual mathematical models in immunobiology are deterministic ones and therefore not applicable to the given problem. We need probabilistic approaches in order to describe the problem properly, because of the huge amount of possible receptor-antigen-combinations and …
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PROBABILISTIC MODELS FOR DEPENDENT VARIABLES: COMPLEX ANALYTIC AND COMBINATORIAL APPROACHES
In this thesis, we study probabilistic models for dependent variables using complex analytic and combinatorial approaches. Our study consists of two independent parts. In the first part, we establish central limit theorems (CLTs) under zero-free conditions, building upon a quantitative extension of …
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Inversion of probabilistic models of structures using measured transfer functions
… for the experimental identification of probabilistic models for the dynamical behaviour of structures. The inversion of probabilistic structural models with minimal parameterization, introduced by Soize, from measured transfer functions is in particular considered. It is first shown that …
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Probabilistic models based on experimental observations using sparse bayes methodology
… provide a useful tool to develop powerful probabilistic models for various problems based on experimental observations.
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