Stochastic Modeling and Bayesian Inference with Applications in Biophysics
Contributors: Kou, Samuel; Meng, Xiao-Li; Blitzstein, Joseph K.
Published: 2012
Language: eng
Description: This thesis explores stochastic modeling and Bayesian inference strategies in the context of the following three problems: 1) Modeling the complex interactions between and within molecules; 2) Extracting information from stepwise signals that are commonly found in biophysical experiments; 3) Improving the computational efficiency of a non-parametric Bayesian inference algorithm.
Notes: Keywords: Bayesian inference, change-points, optional P&oacute;lya tree, single-molecule experiment, stepwise signal, stochastic network model.

Thesis Ph.D. Harvard University 2012