Now showing items 1-7 of 7

    • RafieiSakhaei, MohammadHussein (2017-12-08)
      Simultaneous localization and planning for nonlinear stochastic systems under process and measurement uncertainties is a challenging problem. In its most general form, it is formulated as a stochastic optimal control problem ...
    • Imani, Mahdi (2019-02-25)
      Demand for learning, design and decision making is higher than ever before. Autonomous vehicles need to learn how to ride safely by recognizing pedestrians, traffic signs, and other cars. Companies and consumers need to ...
    • Ghosh, Indrajit (2022-06-15)
      This dissertation focuses on solving some of the most interesting theoretical and methodological questions arising out of various different disciplines with a Bayesian perspective. With the advent of large scale dataset ...
    • Hitchcock, James Mitchell (2011-10-21)
      We investigate the genericity of measure-preserving actions of the free group Fn, on possibly countably infinitely many generators, acting on a standard probability space. Specifically, we endow the space of all ...
    • Rainone, Timothy (2015-08-11)
      This work explores the interplay of C*-dynamics and K-theory. More precisely, we study the extent to which various forms of finite-dimensional approximation properties of a topological nature, witnessed in reduced C*-crossed ...
    • Tyler, Jonathan P. (2019-07-22)
      Biological clocks generate rhythms with periods from seconds to months in many organisms and control many processes that are critical to the survival of the organism. Many rhythms in biology are the result of rhythms in ...
    • Plummer, Sean C (2021-08-02)
      Over the past decade variational inference (VI) has surpassed Markov Chain Monte Carlo (MCMC) as the main method for performing scalable parametric Bayesian inference. Variational inference utilizes optimization to find a ...