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Probability Distribution (2,263 Books)


In probability theory and statistics, a probability distribution identifies either the probability of each value of a random variable (when the variable is discrete), or the probability of the value falling within a particular interval (when the variable is continuous). The probability distribution describes the range of possible values that a random variable can attain and the probability that the value of the random variable is within any (measurable) subset of that range.

 
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Plos One : a Bayesian Approach to Genome, Volume 8

By: Keith A. Crandall

Description : The relationship between the evolution of genes and languages has been studied for over three decades. These studies rely on the assumption that languages, as many other cultural traits, evolve in a gene-like manner, accumulating heritable diversity through time and being subjected to evolutionary mechanisms of change. In the present work we used genetic data to evaluate South American linguistic classifications. We compared discordant models of language cl...

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Plos Computational Biology : a Novel Bayesian Dna Motif Comparison...

By: Ernest Fraenkel

Description : Characterizing the DNA-binding specificities of transcription factors is a key problem in computational biology that has been addressed by multiple algorithms. These usually take as input sequences that are putatively bound by the same factor and output one or more DNA motifs. A common practice is to apply several such algorithms simultaneously to improve coverage at the price of redundancy. In interpreting such results, two tasks are crucial : clustering o...

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Plos Computational Biology : Transmembrane Topology and Signal Pep...

By: Burkhard Rost

Description : Hidden Markov models (HMMs) have been successfully applied to the tasks of transmembrane protein topology prediction and signal peptide prediction. In this paper we expand upon this work by making use of the more powerful class of dynamic Bayesian networks (DBNs). Our model, Philius, is inspired by a previously published HMM, Phobius, and combines a signal peptide submodel with a transmembrane submodel. We introduce a two-stage DBN decoder that combines the...

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Plos Computational Biology : Automatic Annotation of Spatial Expre...

By: Iulian Pruteanu-malinici

Description : Advances in reporters for gene expression have made it possible to document and quantify expression patterns in 2D–4D. In contrast to microarrays, which provide data for many genes but averaged and/or at low resolution, images reveal the high spatial dynamics of gene expression. Developing computational methods to compare, annotate, and model gene expression based on images is imperative, considering that available data are rapidly increasing. We have devel...

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Plos Computational Biology : Bayesian Comparison of Neurovascular ...

By: Maria J. Rosa

Description : Functional magnetic resonance imaging (fMRI), with blood oxygenation level-dependent (BOLD) contrast, is a widely used technique for studying the human brain. However, it is an indirect measure of underlying neuronal activity and the processes that link this activity to BOLD signals are still a topic of much debate. In order to relate findings from fMRI research to other measures of neuronal activity it is vital to understand the underlying neurovascular co...

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Plos Computational Biology : a Bayesian Approach to Analyse Geneti...

By: Trevelyan J. McKinley

Description : The development of modern and affordable sequencing technologies has allowed the study of viral populations to an unprecedented depth. This is of particular interest for the study of within-host RNA viral populations, where variation due to error-prone polymerases can lead to immune escape, antiviral resistance and adaptation to new host species. Methods to sequence RNA virus genomes include reverse transcription (RT) and polymerase chain reaction (PCR). RT...

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Plos Genetics : an Approximate Bayesian Estimator Suggests Strong,...

By: Gil McVean

Description : The recurrent fixation of newly arising, beneficial mutations in a species reduces levels of linked neutral variability. Models positing frequent weakly beneficial substitutions or, alternatively, rare, strongly selected substitutions predict similar average effects on linked neutral variability, if the product of the rate and strength of selection is held constant. We propose an approximate Bayesian (ABC) polymorphism-based estimator that can be used to di...

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Plos One : Accelerating Bayesian Hierarchical Clustering of Time S...

By: Magnus Rattray
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Agency for Healthcare Research and Quality : Use of Bayesian Techn...

By: Agency for Healthcare Research and Quality Ahrq
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Agency for Healthcare Research and Quality : Use of Bayesian Techn...

By: Agency for Healthcare Research and Quality Ahrq
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Plos Computational Biology : Protein Molecular Function Prediction...

By: Jonathan Eisen

Description : We present a statistical graphical model to infer specific molecular function for unannotated protein sequences using homology. Based on phylogenomic principles, SIFTER (Statistical Inference of Function Through Evolutionary Relationships) accurately predicts molecular function for members of a protein family given a reconciled phylogeny and available function annotations, even when the data are sparse or noisy. Our method produced specific and consistent m...

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Plos Computational Biology : a Primer on Learning in Bayesian Netw...

By: Fran Lewitter
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Plos Computational Biology : a Bayesian Partition Mehod for Detect...

By: Wei Zhang
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Plos Genetics : an Approximate Bayesian Estimator Suggests Strong,...

By: Jeannie T. Lee

Description : As genetic information is transmitted through successive generations, it passes between pluripotent cells in the early embryo and germ cells in the developing foetus and adult animal. Tex19.1 encodes a protein of unknown function, whose expression is restricted to germ cells and pluripotent cells. During male spermatogenesis, Tex19.1 expression is highest in mitotic spermatogonia and diminishes as these cells differentiate and progress through meiosis. In p...

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Plos Neglected Tropical Diseases : Bayesian Geostatistical Analysi...

By: Alison P. Galvani

Description : Predictive mapping indicates an increased risk of high HAT prevalence in the future in areas surrounding livestock markets, demonstrating the importance of livestock trading for continuing disease spread. Adherence to government policy to treat livestock at the point of sale is essential to prevent the spread of sleeping sickness in Uganda.

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Plos Neglected Tropical Diseases : Bayesian Geostatistical Modelin...

By: Richard Reithinger

Description : Our spatially explicit, high-resolution incidence maps identified priority areas where leishmaniasis control efforts should be targeted with the ultimate goal to reduce disease incidence.

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Plos One : Bayesian Parameter Estimation and Segmentation in the M...

By: Fabio Rapallo

Description : This paper examines the multiple atlas random diffeomorphic orbit model in Computational Anatomy (CA) for parameter estimation and segmentation of subcortical and ventricular neuroanatomy in magnetic resonance imagery. We assume that there exist multiple magnetic resonance image (MRI) atlases, each atlas containing a collection of locally-defined charts in the brain generated via manual delineation of the structures of interest. We focus on maximum a poster...

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Bayesian Hierarchical Modeling of U. S. County Poverty Rates

By: Robin Fisher

Statistical Reference Document

Excerpt: The U.S. government, thoutgh the Departments of Education, Health and Human Services, Housing and Urban Development, and Labor, allocates approximately $30 billion in funds annually to programs to aid economically disadvantaged areas of the United States.

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Methods for Record Linkage and Bayesian Networks

By: William E. Winkler

Statistical Reference Document

Introduction: Record linkage is the science of finding matches or duplicates within or across files. Matches are typically delineated using name, address, and date-of-birth information. Other identifiers such as income, education, and credit information might be used. With a pair of records, identifiers might not correspond exactly. For instance, income in one record might be compared to mortgage payment size using a crude regression function. In the computer science lit...

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Bayesian Networks Representations, Generalized Imputation, And Syn...

By: Yves Thibaudeau

Statistical Reference Document

Introduction: Graphical representation of Bayes Nets and other probabilistic relationships date to Lauritzen and Spiegelhalter (1988). They are used extensively in machine learning. For instance, Figure 2 in Getoor et al. (2001) (reprinted below) demonstrates an efficient representation of Census data. 951 parameters are able to represent a potentially large number of cells in a contingency table (7 billion). Bayes Net software will quickly determine dependency relations...

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