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.
Results
Showing 1 to 20 of 50 for “"probabilistic modeling"”.
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Probabilistic modeling of planar pushing
This work studies the problem of data-driven modeling and stochastic filtering of complex dynamical systems. The main contributions are GP-SUM, a filtering algorithm tailored to systems expressed as Gaussian processes (GP), and the probabilistic modeling of planar pushing by combining …
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Probabilistic Modeling of Shallow Landslides at Regional Scales
… area (Chapter 2). Chapter 3 describes a new probabilistic model of shallow landsliding based on a physical model that is coupled with a macro-scale hydrologic model and a soil evolution model explicitly addressing spatial and temporal uncertainty. This physical model is integrated with a …
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Probabilistic Modeling and Verification of Large Scale Systems
… statistically validates the model. We develop a probabilistic temporal logic called iLTL in order to specify aggregate behaviors of large scale systems. Many important performance criteria on DTMC models can be accurately expressed in iLTL. For the iLTL specifications, performance evaluation is …
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Probabilistic modeling and Bayesian inference via triangular transport
Probabilistic modeling and Bayesian inference in non-Gaussian settings are pervasive challenges for science and engineering applications. Transportation of measure provides a principled framework for treating non-Gaussianity and for generalizing many methods that rest on Gaussian assumptions. A …
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Probabilistic modeling of kidney dynamics for renal failure prediction
… more effective. In this thesis, we developed probabilistic models for modeling variable kinetics and temporal dynamics of states. We applied the models to the prediction of acute kidney injury (AKI), but the models are applicable to other medical conditions as well. It is known that serum …
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Probabilistic Modeling of Multi-relational and Multivariate Discrete Data
Modeling and discovering knowledge from multi-relational and multivariate discrete data is a crucial task that arises in many research and application domains, e.g. text mining, intelligence analysis, epidemiology, social science, etc. In this dissertation, we study and address three problems …
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Diffusion Probabilistic Modeling of Protein Backbones in 3D for the Motif-Scaffolding problem
Construction of a scaffold structure that supports a desired motif, conferring protein function, shows promise for the design of vaccines and enzymes. But a general solution to this motif-scaffolding problem remains open. Current machine-learning techniques for scaffold design are either limited to …
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Probabilistic modeling of the drug development domain: A Bayesian domain-knowledge application for pharmacovigilance
… with developing a Bayesian domain-knowledge probabilistic model (called Pharminator) to address the first two of these categories, with a goal of predicting clinical success of an NCE. Pharmacoeconomic modeling is a vastly different domain compared to Pharminator's clinical trial domain, and …
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Quantitative probabilistic modeling of environmental control and life support System resilience for long-duration human spaceflight
The future of human space exploration will see crews travel farther and remain in space for longer durations than ever before. For the first time in the history of human spaceflight, the Environmental Control and Life Support Systems (ECLSS) that sustain the crew in their habitat will have to …
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Influence of randomness in mathematical models for diffraction and T-cell recognition
… system. In this part the main focus lies in the probabilistic modeling itself, preparation of the mathematical tools, and the analysis to show the ability of the models to explain the reliable recognition of foreign invaders by certain white blood cells.
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Determining the value of volume and process flexibility in market driven manufacturing operations
… evolutionary stage and operational objectives. Probabilistic modeling, linear optimization and iterative simulation is used to show that volume and product mix flexibility can substantially improve the ability to respond to customer demands while improving capacity utilization. Network flow …
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Advances in Probabilistic Deep Learning and Their Applications
Deep learning and probabilistic modeling are two machine learning paradigms with complementary benefits. Probabilistic deep learning aims to unify the two, with the potential to offer compelling theoretical properties and practical functional benefits across a variety of problems. This thesis …
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Algorithms for 3D time-of-flight imaging
… (GAMP) framework to incorporate both accurate probabilistic modeling for the measurement process and underlying scene depth map sparsity to accurately extend the unambiguous depth range of the camera. This joint processing results in improved performance over separate unwrapping and denoising …
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Walking forward and backward: towards graph-based searching and mining
… we enhance extraction with the metrics of probabilistic precision and recall, ranking with the senses of importance and specificity, and classification with heterogeneous contexts in terms of relationship type and confidence level. We further study the underpinning principle of random walks …
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A mathematical model of polymerase chain reaction induced stutter
… this thesis is achieved through data analysis, probabilistic modeling, and statistics. We find that a mathematical model that combines stochastic effects from PCR with fluorescent noise explains the most significant features of the observed phenomena.
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Probabilistic design for emerging memory and nanometer-scale logic
… classes of problems that require the use of probabilistic methods and models: (1) Modeling and exploiting stochastic behavior in advanced memory technologies; (2) Probabilistic modeling of faults due to on-chip voltage variation. This dissertation first investigates the unique physics-level …
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Sto(ry)chastics : a Bayesian network architecture for combined user modeling, sensor fusion, and computational storytelling for interactive spaces
… framework for real-time sensor-driven stochastic modeling of story and user-story interaction, which I call sto(ry)chastics. Almost all sensor-driven interactive entertainment, art, and architecture installations today rely on one-to-one mappings between content and participant's actions to tell a …
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