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Assembly of protein tertiary structures from fragments with similar local sequences using simulated annealing and Bayesian scoring functions

by Kim T. Simons, Charles Kooperberg, Enoch Huang, David Baker - J. MOL. BIOL , 1997
"... We explore the ability of a simple simulated annealing procedure to assemble native-like structures from fragments of unrelated protein structures with similar local sequences using Bayesian scoring functions. Environment and residue pair specific contributions to the scoring functions appear as the ..."
Abstract - Cited by 393 (70 self) - Add to MetaCart
We explore the ability of a simple simulated annealing procedure to assemble native-like structures from fragments of unrelated protein structures with similar local sequences using Bayesian scoring functions. Environment and residue pair specific contributions to the scoring functions appear

BioOptimizer: a Bayesian scoring function approach to motif discovery

by Shane T. Jensen, Jun S. Liu - Bioinformatics
"... Motivation: Transcription factors (TFs) bind directly to short segments on the genome, often within hundreds to thousands of base pairs upstream of gene transcription start sites, to regulate gene expression. The experimental determination of TFs binding sites is expensive and time-consuming. Many m ..."
Abstract - Cited by 41 (10 self) - Add to MetaCart
full Bayesian model that can handle unknown site abund-ance, unknown motif width and two-block motifs with variable-length gaps. An algorithm called BioOptimizer is proposed to optimize this scoring function so as to reduce noise in the motif signal found by any motif-finding program. The accur

Recognizing Complex, Asymmetric Functional Sites In Protein Structures Using A Bayesian Scoring Function

by Liping Wei, L. Wei, R. B. Altman - J Bioinform Comput Biol , 2003
"... this paper, we report a new, improved FEATURE system and investigate the FEATURE's ability to recognize sites that are geometrically complex and asymmetric, such as ATP-binding sites, redoxin active sites, and disulfide bond-forming sites. We also show that, if a biologist does not have enough ..."
Abstract - Cited by 5 (0 self) - Add to MetaCart
this paper, we report a new, improved FEATURE system and investigate the FEATURE's ability to recognize sites that are geometrically complex and asymmetric, such as ATP-binding sites, redoxin active sites, and disulfide bond-forming sites. We also show that, if a biologist does not have enough known examples of a site to build a statistical profile, she can use aprioriknowledge to construct an artificial profile for the site and FEATURE can use the artificially constructed profile to search for the site in new structures

Bayesian Analysis of Stochastic Volatility Models

by Eric Jacquier, Nicholas G. Polson, Peter E. Rossi , 1994
"... this article is to develop new methods for inference and prediction in a simple class of stochastic volatility models in which logarithm of conditional volatility follows an autoregressive (AR) times series model. Unlike the autoregressive conditional heteroscedasticity (ARCH) and gener- alized ARCH ..."
Abstract - Cited by 601 (26 self) - Add to MetaCart
ARCH (GARCH) models [see Bollerslev, Chou, and Kroner (1992) for a survey of ARCH modeling], both the mean and log-volatility equations have separate error terms. The ease of evaluating the ARCH likelihood function and the ability of the ARCH specification to accommodate the timevarying volatility

Learning Bayesian networks: The combination of knowledge and statistical data

by David Heckerman, David M. Chickering - Machine Learning , 1995
"... We describe scoring metrics for learning Bayesian networks from a combination of user knowledge and statistical data. We identify two important properties of metrics, which we call event equivalence and parameter modularity. These properties have been mostly ignored, but when combined, greatly simpl ..."
Abstract - Cited by 1158 (35 self) - Add to MetaCart
We describe scoring metrics for learning Bayesian networks from a combination of user knowledge and statistical data. We identify two important properties of metrics, which we call event equivalence and parameter modularity. These properties have been mostly ignored, but when combined, greatly

Sparse Bayesian Learning and the Relevance Vector Machine

by Michael E. Tipping , 2001
"... This paper introduces a general Bayesian framework for obtaining sparse solutions to regression and classification tasks utilising models linear in the parameters. Although this framework is fully general, we illustrate our approach with a particular specialisation that we denote the `relevance vect ..."
Abstract - Cited by 966 (5 self) - Add to MetaCart
vector machine’ (RVM), a model of identical functional form to the popular and state-of-the-art `support vector machine ’ (SVM). We demonstrate that by exploiting a probabilistic Bayesian learning framework, we can derive accurate prediction models which typically utilise dramatically fewer basis

Stochastic relaxation, Gibbs distributions and the Bayesian restoration of images.

by Stuart Geman , Donald Geman - IEEE Trans. Pattern Anal. Mach. Intell. , 1984
"... Abstract-We make an analogy between images and statistical mechanics systems. Pixel gray levels and the presence and orientation of edges are viewed as states of atoms or molecules in a lattice-like physical system. The assignment of an energy function in the physical system determines its Gibbs di ..."
Abstract - Cited by 5126 (1 self) - Add to MetaCart
Abstract-We make an analogy between images and statistical mechanics systems. Pixel gray levels and the presence and orientation of edges are viewed as states of atoms or molecules in a lattice-like physical system. The assignment of an energy function in the physical system determines its Gibbs

Games with Incomplete Information Played by 'Bayesian' Players, I-III

by John C Harsanyi - MANAGEMENT SCIENCE , 1967
"... The paper develops a new theory for the analysis of games with incomplete information where the players are uncertain about some important parameters of the game situation, such as the payoff functions, the strategies available to various players, the information other players have about the game, e ..."
Abstract - Cited by 787 (2 self) - Add to MetaCart
The paper develops a new theory for the analysis of games with incomplete information where the players are uncertain about some important parameters of the game situation, such as the payoff functions, the strategies available to various players, the information other players have about the game

Experimental Estimates of Education Production Functions

by Alan B. Krueger - Princeton University, Industrial Relations Section Working Paper No. 379 , 1997
"... This paper analyzes data on 11,600 students and their teachers who were randomly assigned to different size classes from kindergarten through third grade. Statistical methods are used to adjust for nonrandom attrition and transitions between classes. The main conclusions are (1) on average, performa ..."
Abstract - Cited by 529 (19 self) - Add to MetaCart
, performance on standardized tests increases by four percentile points the �rst year students attend small classes; (2) the test score advantage of students in small classes expands by about one percentile point per year in subsequent years; (3) teacher aides and measured teacher characteristics have little

Speaker verification using Adapted Gaussian mixture models

by Douglas A. Reynolds, Thomas F. Quatieri, Robert B. Dunn - Digital Signal Processing , 2000
"... In this paper we describe the major elements of MIT Lincoln Laboratory’s Gaussian mixture model (GMM)-based speaker verification system used successfully in several NIST Speaker Recognition Evaluations (SREs). The system is built around the likelihood ratio test for verification, using simple but ef ..."
Abstract - Cited by 1010 (42 self) - Add to MetaCart
but effective GMMs for likelihood functions, a universal background model (UBM) for alternative speaker representation, and a form of Bayesian adaptation to derive speaker models from the UBM. The development and use of a handset detector and score normalization to greatly improve verification performance
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