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 20 for “"Bayesian theory"”.
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Bayesian Theory of Mind : modeling human reasoning about beliefs, desires, goals, and social relations
… computational framework for understanding human Theory of Mind (ToM): our conception of others' mental states, how they relate to the world, and how they cause behavior. Humans use ToM to predict others' actions, given their mental states, but also to do the reverse: attribute mental states - …
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A Bayesian theory of mind approach to nonverbal communication for human-robot interactions : a computational formulation of intentional inference and belief manipulation
Much of human social communication is channeled through our facial expressions, body language, gaze directions, and many other nonverbal behaviors. A robot's ability to express and recognize the emotional states of people through these nonverbal channels is at the core of artificial social …
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Epistemic Rationality and Justification
… to either reject or continue to accept Newton's theory before the proposal of Einstein's general theory of relativity. I then examine three major conceptions of justification and argue that if justification is to be truth-conducive, then theories of justification based on these three conceptions …
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Reliability of geotechnical systems considering geological anomaly
… a systematic reliability evaluation. Based on Bayesian theory, methods are developed whereby the statistics of anomaly properties can be assessed from previous experience updated by observations from additional site exploration programs. Furthermore, the effect of anomalies in the geotechnical …
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A theory of (almost) zero resource speech recognition
… text to estimate meaningful language models. A theory of unsupervised and semi-supervised techniques for speech recognition is therefore essential. This thesis focuses on HMM-based sequence clustering and examines acoustic modeling, language modeling, and applications beyond the components of an …
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Bayesian Calibration for Logit Model Microsimulations: Case for PECAS SD in San Diego
Bayesian inference is a versatile method for incorporating new information into a model while still respecting existing knowledge. One application of Bayesian inference is the calibration of models that are controlled by a large number of parameters, but where the data usable for calibration is …
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Aplicação de modelos regionais e conceituais em estudos hidroenergéticos: uma abordagem bayesiana
… is general and may be developed for other cases. Bayesian theory is the framework from which the main results are derived. Background material of this theory for independent and normally distributed mean anual flows, are presented in concise form. Other stochastic models are not considered. The …
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Neural Encoding of Prior Experience in Sensorimotor Behavior
… regularities is often described by the Bayesian theory in terms of prior distributions that represent knowledge previously gathered about the environment. At the neural scale, the effects of prior experience have been described by the theory of predictive processing in terms of efficient …
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Graphical tools for the examination of high-dimensional functions obtained as the result of Bayesian analysis
Bayesian statistics has a tendency to produce objects that are of many more than three dimensions, typically of the same dimensionality as the parameter set of the problem. This thesis takes the idea of visual, exploratory data analysis and attempts to apply it to those objects. In order to do this …
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Semantics and statistics for automated image annotation
… to images. In particular, approaches based on Bayesian theory use machine-learning techniques to learn statistical models from a training set of pre-annotated images and apply them to generate annotations for unseen images. <br></br><br></br> The focus of this thesis lies in demonstrating that …
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Scaling Cooperative Intelligence via Inverse Planning and Probabilistic Programming
… programming architecture that implements a Bayesian theory of mind. This architecture, Sequential Inverse Plan Search (SIPS), performs online inference of human goals and plans by inverting a Bayesian model of incremental human planning. By combining high-performance symbolic planners with …
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Bayesian algorithms for speech enhancement
… The algorithms are derived from the Bayesian theory of estimation and can be grouped according to i) the STFT representation they estimate ii) the estimator they apply and iii) the speech prior density they assume. Apart from the introduction of algorithms that surpass the performance …
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Imprecise probability and decision in civil engineering : Dempster-Shafer theory and application
Over the last three decades, Bayesian theory has been widely adopted in civil engineering for dealing with uncertainty and for purposes of decision making under uncertainty. However the Bayesian approach is not without criticisms. One major concern has been that information or knowledge, no matter …
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ML-Based Optimization of Large-Scale Systems: Case Study in Smart Microgrids and 5G RAN
… failures will affect MG energy trading by using Bayesian deep reinforcement learning (BA-DRL). On the 5G side, we use MARL, transfer reinforcement learning (TRL), and hierarchical reinforcement learning (HRL) to improve network performance. In particular, we study the performance of those …
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Essays on the Bayesian inequality restricted estimation
Bayesian estimation has gained ground after Markov Chain Monte Carlo process made it possible to sample from exact posterior distributions. This research aims at contributing to the ongoing debate about the relative virtues of the Frequentist and Bayesian theories by concentrating on the …
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Semantically aware hierarchical Bayesian network model for knowledge discovery in data : an ontology-based framework
… framework that integrates the Hierarchical Bayesian Network (HBN) and domain ontology. The ultimate aim of this thesis is to propose a data mining framework that implicitly caters for the underpinning domain knowledge and eventually leads to a more intelligent and accurate mining process. To …
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Cognitive Human Activity and Plan Recognition for Human-Robot Collaboration
… a combined model for HAPR that captures the Bayesian theory of mind (BToM) from cognitive science. This thesis presents a cognitive HAPR system called cognitively motivated plan and activity estimation system (COMPASS) that achieves the three ideas. We evaluate COMPASS in a home care …
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High-Dimensional Optimal Path Planning and Multi-Timescale Lagrangian Data Assimilation in Stochastic Dynamical Ocean Environments
… in the science of autonomy involve fundamental theory, rigorous methods, and efficient computations for autonomous systems that collect information, learn, collaborate and make decisions under uncertainty, all in optimal integrated fashion and over long duration, persistently adapting to and …
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A Bayesian framework for concept learning
… from examples, based on the principles of Bayesian inference. By imposing the constraints of a probabilistic model of the learning situation, the Bayesian learner can draw out much more information about a concept's extension from a given set of observed examples than either rule-based or …
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Mrs. Dalloway as a Window for Understanding Life
<p>Virginia Woolf’s Mrs. Dalloway may be dismissed as fiction, and fiction consequently is dismissed as fantasy. However, the novel enables readers to practice an intellectual exercise of meta-awareness that extends beyond the pages and onto real world phenomena. Under a cognitive neuroscience …