5 papers
Improving score-based sampling via affine post-processing
Curtis McDonald, Kevin Rojas, Molei Tao +1
Sampling from a probability distribution with access only to its log-density is a foundational problem in statistics, machine learning, and the physical sciences. This problem is s…
Proposal of a Score Based Approach to Sampling Using Monte Carlo Estimation of Score and Oracle Access to Target Density
Curtis McDonald, Andrew Barron
Score based approaches to sampling have shown much success as a generative algorithm to produce new samples from a target density given a pool of initial samples. In this work, we…
Robustness to Incorrect Priors and Controlled Filter Stability in Partially Observed Stochastic Control
Curtis McDonald, Serdar Yüksel
We study controlled filter stability and its effects on the robustness properties of optimal control policies designed for systems with incorrect priors applied to a true system. F…
Exponential Filter Stability via Dobrushin's Coefficient
Curtis McDonald, Serdar Yuksel
Filter stability is a classical problem in the study of partially observed Markov processes (POMP), also known as hidden Markov models (HMM). For a POMP, an incorrectly initialized…
Stochastic Observability and Filter Stability under Several Criteria
Curtis McDonald, Serdar Yuksel
Despite being a foundational concept of modern systems theory, there have been few studies on observability of non-linear stochastic systems under partial observations. In this pap…