Inferential structure determination.

Inferential Structure Determination of Chromosomes from Single-Cell Hi-C Data

Chromosome conformation capture (3C) techniques have revealed many fascinating insights into the spatial organization of genomes. 3C methods typically provide information about chromosomal contacts in a large population of cells, which makes it difficult to draw conclusions about the three-dimensional organization of genomes in individual cells. Recently it became possible to study single cells...

Stochastic Determination of the Intrinsic Structure in Bayesian Factor Analysis

The study of factor analytic models often has to address two important issues: (a) the determination of the ”optimum” number of factors and (b) the derivation of a unique simple structure whose interpretation is easy and straightforward. The classical approach deals with these two tasks separately, and sometimes resorts to ad-hoc methods. This paper proposes a Bayesian approach to these two imp...

Inferential NMR/X-ray-based structure determination of a dibenzo[a,d]cycloheptenone inhibitor-p38α MAP kinase complex in solution.

Determining the Model Order of Nonlinear Input/output Systems Directly from Data

The method of false nearest neighbors (FNN) is presented here as a tool for analyzing the \dimensionali-ty" of a nonlinear input/output system directly from data. A unique feature of this method is that no speciic model structure is assumed, the dimensional determination is made directly from topological considerations. An extension to the FNN algorithm is given to consider the problem of infer...

Corporatized Higher Education: A Quantitative Study Examining Faculty Motivation Using Self-Determination Theory

....................................................................................................................................................... iii Acknowledgments ........................................................................................................................................ iv Dedication .............................................................................

Generative probabilistic models extend the scope of inferential structure determination.

Conventional methods for protein structure determination from NMR data rely on the ad hoc combination of physical forcefields and experimental data, along with heuristic determination of free parameters such as weight of experimental data relative to a physical forcefield. Recently, a theoretically rigorous approach was developed which treats structure determination as a problem of Bayesian inf...