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 11 of 11 for “"Bayesian Experimental Design"”.
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Accounting for Computational Expenditures in Bayesian Experimental Design
… for the data collection process is known as experimental design. The Bayesian optimal experimental design (BOED) formulation uses Bayesian inference to update beliefs after observing data and optimizes a utility function – most commonly mutual information – and is computationally challenging. …
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Accelerated Bayesian experimental design for chemical kinetic models
The optimal selection of experimental conditions is essential in maximizing the value of data for inference and prediction, particularly in situations where experiments are time-consuming and expensive to conduct. A general Bayesian framework for optimal experimental design with nonlinear …
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A combinatorial approach to goal-oriented optimal Bayesian experimental design
Optimal experimental design plays an important role in science and engineering. In many situations, we have many observations but only few of them can be selected due to limited resources. We then need to decide which ones to select based on our goal. In this thesis, we study the Bayesian linear …
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Optimal Bayesian experimental design in the presence of model error
The optimal selection of experimental conditions is essential to maximizing the value of data for inference and prediction. We propose an information theoretic framework and algorithms for robust optimal experimental design with simulation-based models, with the goal of maximizing information gain …
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Demonstration of Bayesian inference and Bayesian experimental design in a model film/substrate inference problem
In this thesis, we implement Bayesian inference and Bayesian experiment design in a model materials science problem. We demonstrate that by observing the behavior of a film deposited on a substrate, certain features of the substrate may be inferred, with quantified uncertainty. We show that …
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On the advancement of optimal experimental design with applications to infectious diseases.
In this thesis, we investigate the optimal experimental design of some common biological experiments. The theory of optimal experimental design is a statistical tool that allows us to determine the optimal experimental protocol to gain the most information about a particular process, given …
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Multiple objective resource allocation in product and process development
… resources for information gathering is based on Bayesian experimental design. Specifically, experimental designs for parameter estimation, model discrimination, and decisionmaking have been examined. Solving some of these design problems rigorously has not previously been attempted due to the …
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Bayesian causal inference of cell signal transduction from proteomics experiments
… methods for causal inference to develop causal Bayesian network models of signal transduction from experimental data. Directed edges in the network represent causal regulatory relationships, and the model can be used to predict the effects of interventions to signal transduction. Causal …
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Bayesian Inference and Experimental Design of Combustion Kinetic Models
… model calibration, researchers usually use experimental data to reduce the uncertainty of kinetic parameters, and Bayesian inference is the most common approach to do inverse calibration. This thesis explores two interconnected aspects of Bayesian approaches in the context of combustion …
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Towards a psychological science of neural network behaviour
… to the prototypical science of behaviour: experimental psychology. Drawing on experimental psychology along epistemological, methodological and metascientific lines, this thesis will explore ideas including the differing nature of explanatory practices, the role of experimental design and a …
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BAYESIAN SEQUENTIAL OPTIMAL EXPERIMENTAL DESIGN FOR INVERSE PROBLEMS USING DEEP REINFORCEMENT LEARNING
We perform a comprehensive study on Bayesian sequential optimal experimental design techniquesapplied to inverse problems. We transform the Bayesian sequential optimal experimental design problem into a reinforcement learning problem to gauge the power of recent deep reinforcement learning …