… , 1977), the negative contribution from the exposure of polar surface area has, to our
knowledge, only recently been considered in discussions of the ACE of denaturation (Murphy and …

A comprehensive introduction to machine learning that uses probabilistic models and
inference as a unifying approach. Today's Web-enabled deluge of electronic data calls for …

INFORMATION TO USERS Page 1 INFORMATION TO USERS This manuscript has been
reproduced from the microfilm master. UMI films the text directly from the original or copy …

… The dotted line corresponds to the analysis of a smaller data set by Murphy and Gill 34 with
T H * = 100.5 C and ΔH* = 5.64 kJ (mol res) -1 . The set of Spolar etal. gives T H * = 84 C and …

We consider the problem of learning a grid-based map using a robot with noisy sensors and
actuators. We compare two approaches: online EM, where the map is treated as a fixed …

A detailed and up-to-date introduction to machine learning, presented through the unifying
lens of probabilistic modeling and Bayesian decision theory. This book offers a detailed and …

The Gaussian or normal distribution is one of the most widely used in statistics. Estimating its
parameters using Bayesian inference and conjugate priors is also widely used. The use of …

Protein unfolding and the dissolution of hydrophobic compounds (including solids, liquids,
and gases) in water are characterized by a linear relation between entropy change and heat …

A classifier is a function f that maps input feature vectors x∈ X to output class labels y∈{1,...,
C}, where X is the feature space. We will typically assume X= IRD or X={0, 1} D, ie, that the …

We seek to build a large collection of images with ground truth labels to be used for object
detection and recognition research. Such data is useful for supervised learning and …