PHD Project - Driver energy prediction
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\contentsline {chapter}{List of Figures}{xi}{section*.5}%
\contentsline {chapter}{List of Tables}{xiii}{section*.7}%
\contentsline {chapter}{Notational Conventions}{xv}{section*.10}%
\contentsline {chapter}{Acronyms and Abbreviations}{xvii}{section*.13}%
\contentsline {chapter}{\numberline {1}Introduction}{1}{chapter.1}%
\contentsline {section}{\numberline {1.1}Context and motivation}{1}{section.1.1}%
\contentsline {section}{\numberline {1.2}Survey of related work}{2}{section.1.2}%
\contentsline {section}{\numberline {1.3}Research objectives and thesis outline}{2}{section.1.3}%
\contentsline {chapter}{\numberline {2}Background / Theory}{3}{chapter.2}%
\contentsline {section}{\numberline {2.1}The Bayesian Framework}{3}{section.2.1}%
\contentsline {subsection}{\numberline {2.1.1}Modeling via Exchangeability}{3}{subsection.2.1.1}%
\contentsline {section}{\numberline {2.2}Exponential Families}{7}{section.2.2}%
\contentsline {subsection}{\numberline {2.2.1}Properties of the Canonical Exponential Family}{9}{subsection.2.2.1}%
\contentsline {subsection}{\numberline {2.2.2}Interpretation as Linearly Constrained Maximum Entropy Distribution}{9}{subsection.2.2.2}%
\contentsline {subsection}{\numberline {2.2.3}Examples}{9}{subsection.2.2.3}%
\contentsline {section}{\numberline {2.3}Sufficient Statistics}{9}{section.2.3}%
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\contentsline {subsection}{\numberline {2.4.3}Gaussian Observations}{11}{subsection.2.4.3}%
\contentsline {subsection}{\numberline {2.4.4}Multivariate Linear Regression Model}{11}{subsection.2.4.4}%
\contentsline {section}{\numberline {2.5}Graphical Models}{11}{section.2.5}%
\contentsline {subsection}{\numberline {2.5.1}A Brief Overview}{11}{subsection.2.5.1}%
\contentsline {subsection}{\numberline {2.5.2}Directed Graphical Models}{11}{subsection.2.5.2}%
\contentsline {subsection}{\numberline {2.5.3}Undirected Graphical Models}{11}{subsection.2.5.3}%
\contentsline {subsection}{\numberline {2.5.4}Belief Propagation}{11}{subsection.2.5.4}%
\contentsline {section}{\numberline {2.6}Hidden Markov Model}{11}{section.2.6}%
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\contentsline {subsection}{\numberline {2.7.2}Vector Autoregressive Processes}{11}{subsection.2.7.2}%
\contentsline {subsection}{\numberline {2.7.3}Switching Linear Dynamic Systems}{11}{subsection.2.7.3}%
\contentsline {subsection}{\numberline {2.7.4}Stochastic Realization Theory}{11}{subsection.2.7.4}%
\contentsline {subsection}{\numberline {2.7.5}Kalman Filtering and Smoothing}{11}{subsection.2.7.5}%
\contentsline {section}{\numberline {2.8}Markov Chain Monte Carlo}{11}{section.2.8}%
\contentsline {subsection}{\numberline {2.8.1}Monte Carlo Integration}{11}{subsection.2.8.1}%
\contentsline {subsection}{\numberline {2.8.2}The Metropolis-Hastings Algorithm}{11}{subsection.2.8.2}%
\contentsline {subsection}{\numberline {2.8.3}Gibbs Sampling}{11}{subsection.2.8.3}%
\contentsline {subsection}{\numberline {2.8.4}Auxiliary, Blocked, and Collapsed Gibbs Samplers}{11}{subsection.2.8.4}%
\contentsline {section}{\numberline {2.9}Bayesian Nonparametric Methods}{11}{section.2.9}%
\contentsline {subsection}{\numberline {2.9.1}Dirichlet Processes}{11}{subsection.2.9.1}%
\contentsline {subsection}{\numberline {2.9.2}Dirichlet Process Mixture Models}{11}{subsection.2.9.2}%
\contentsline {subsection}{\numberline {2.9.3}Hierarchical Dirichlet Processes}{11}{subsection.2.9.3}%
\contentsline {subsection}{\numberline {2.9.4}Beta Process}{11}{subsection.2.9.4}%
\contentsline {chapter}{\numberline {3}Route/Trajectory Prediction}{13}{chapter.3}%
\contentsline {section}{\numberline {3.1}Section}{13}{section.3.1}%
\contentsline {subsection}{\numberline {3.1.1}Subsection}{13}{subsection.3.1.1}%
\contentsline {chapter}{\numberline {4}Prediction of driver influenced parameter (velocity and longitudinal and lateral acceleration)}{15}{chapter.4}%
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