State estimation consists of updating an agent’s belief given executed actions and observed evidence to date. In single agent environments, the state estimation can be formalize...
A novel nonlinear scale space framework is proposed for the purpose of multiscale image representation. The scale space decomposition problem is formulated as a general Bayesian l...
Akshaya Kumar Mishra, Alexander Wong, David A. Cla...
We present a novel method for predicting the secondary structure of a protein from its amino acid sequence. Most existing methods predict each position in turn based on a local wi...
Scott C. Schmidler, Jun S. Liu, Douglas L. Brutlag
We propose an unbounded-depth, hierarchical, Bayesian nonparametric model for discrete sequence data. This model can be estimated from a single training sequence, yet shares stati...
Analysis of biopolymer sequences and structures generally adopts one of two approaches: use of detailed biophysical theoretical models of the system with experimentally-determined...
Scott C. Schmidler, Joseph E. Lucas, Terrence G. O...