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Showing 1 to 20 of 43 for “"posterior probabilities"”.
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Fast program for sequence alignment using partition function posterior probabilities
The key requirements of a good sequence alignment tool are high accuracy and fast execution. The existing Probalign program is a highly accurate tool for sequence alignment of both proteins and nucleotides. However, the time for execution is fairly high. The focus is therefore, to reduce the …
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The Relative Efficiency of Replicates in Sweet Corn Hybrid Disease Nurseries and Posterior Probabilities as a Measure of Confidence in Assigning Sweet Corn Hybrids to Disease Reaction Categories
… into reaction categories and to calculate posterior probabilities with Bayes' theorem as a measure of confidence in assigning hybrids to reaction categories based on single or multiple trials. For each of three diseases (northern leaf blight, Stewart's wilt, and common rust), 1,000 …
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Bayesian Interaction and Association Networks From Multiple Replicates of Sparse Time-Course Data
… time-course data, protein (or gene) interaction posterior probabilities are computed based on individual and multiple replicates. This is accomplished through Bayesian inference in conjunction with the Metropolis-Hastings algorithm. The Bayesian posterior probability is computed for two distinct …
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Bayesian Model Uncertainty and Prior Choice with Applications to Genetic Association Studies
… MISA, allows computation of multilevel posterior probabilities and Bayes factors at the global, gene and SNP level. We use simulated data sets to characterize MISA's statistical power, and show that MISA has higher power to detect association than standard procedures. Using data from the …
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Efficient hand orientation and pose estimation for uncalibrated cameras
… was used to train the next expert regressor. The posterior probabilities for each training sample were extracted from each expert regressors. These posterior probabilities were then used along with a Kullback-Leibler divergence-based optimization method to estimate the marginalization weights for …
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Advances in approximate inference: combining VI and MCMC and improving on Stein discrepancy
… is rooted in the intractability of computing posterior probabilities. Approximate inference provides an alternative workaround by providing a tractable estimate of posterior probabilities. The performance of Bayesian inference, especially in Bayesian deep learning, crucially depends on the …
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A Probabilistic Approach To Multiple-Instance Learning
… classication algorithms were proposed where posterior probabilities were estimated under dierent assumptions. The rst algorithm, named instance-vote, assumes that the probability of a bag being positive or negative depends upon the percentage of its instances being positive or negative. This …
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Decision Theory Classification Of High-dimensional Vectors Based On Small Samples
… is based on the Bayesian paradigm and provides posterior probabilities that a new vector belongs to each of the classes, therefore it adapts naturally to any number of classes. Our classification technique is based on a small vector which is related to the projection of the observation onto the …
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A comparative study of the effectiveness of two Bayesian models for predicting the academic successes of selected allied health students enrolled in the comprehensive community college
… and evaluate Bayesian-type models for estimating probabilities of program completion and predicting first quarter grade point average (GPA). Bayesian Model 1 Estimating Probabilities of Program Completion was developed from the discrete case of Bayes' formula with counselors' inputs as a priori …
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Applications and Computation of Stateful Polya Trees
… variables associated with it. We can learn the posterior distributions of these state variables along with the posterior of the distribution. State variables may be of interest in their own right, or may be nuisance parameters which we use to achieve more flexible models but wish to integrate …
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Robust synthetic control
… the uncertainty of their model/estimates through posterior probabilities. Our empirical results demonstrate that our robust generalization yields a positive impact over the classical synthetic control method, underscoring the value of our key de-noising procedure.
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Automatic Detection of Landmark Acoustic Cues in Human Speech
… of each landmark. Using Bayes’ Theorem, the posterior probabilities are calculated to determine the most probable landmark (or absence thereof) at each time frame. The system’s performance is evaluated by comparing the detected landmarks to the manually labeled ground truth landmark …
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ESSAYS ON EMPIRICAL ASSET PRICING USING BAYESIAN METHODS
… from the statistics literature to estimate posterior probabilities of asset pricing factors using many assets at once. Using a dataset of thousands of individual stocks in the US market, we calculate posterior probabilities of 12 factors which have been suggested in the literature. Our …
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Improving Neural Network Classification Training
… a learning model to output more accurate posterior probabilities. This algorithm is used to improve the reliability of classification-based networks while retaining their higher degree of classification accuracy. These approaches are demonstrated to be robust to a variety of learning …
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Information dissemination and corporate bankruptcies
… data and improved measures of high-frequency posterior probabilities of informed trading, I document a substantial increase in informed selling several days before bankruptcy announcements. This pre-announcement informed selling attenuates subsequent announcement returns, suggesting that part …
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Input of Factor Graphs into the Detection, Classification, and Localization Chain and Continuous Active SONAR in Undersea Vehicles
… passing algorithm is applied to compute the posterior probabilities at a particular node. This thesis addresses two issues. In the first section, the formulation of factor graphs for each section of the DCL chain required followed by their closed-form solutions. For the detector, the factor …
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Extracting more wisdom from the crowd
… confidence, or, more generally, on Bayesian posterior probabilities. Our model suggests a new method for aggregating opinions: select the answer that is more popular than people predict. We derive theoretical conditions under which this new method is guaranteed to work, and generalize it to …
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Bayesian Model Averaging and Variable Selection in Multivariate Ecological Models
… models and obtaining esti-mates of their posterior probabilities via Markov chain Monte Carlo (MCMC). These probabilities can be further used as weights for model averaged predictions and estimates of the parameters of interest. As a result, variance components due to model selection are …
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Genetic variation and parentage in the Ethiopian wolf Canis simensis
… through software analysis in Colony found posterior probabilities of no less than 1.00 for all six offspring individuals analysed. The parentage assignments revealed that offspring regularly moved between packs, which may be attributed to the loss of individuals through rabies during the …
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Advances in Iterative Probabilistic Processing for Communication Receivers
… processing is to approximate maximum a posteriori (MAP) symbol-by-symbol detection of the information bits and estimation of the unknown channel or signal parameters. The sum-product algorithm is capable of efficiently approximating the marginal posterior probabilities desired for MAP …
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