Search Results: 'Bayesian data analysis'

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3 results for "Bayesian data analysis"
Probabilistic Interpretation of Data By Guthrie Miller
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This book is a physicists approach to interpretation of data using Markov Chain Monte Carlo (MCMC). The concepts are derived from first principles using a style of mathematics that quickly... More > elucidates the basic ideas, sometimes with the aid of examples. Probabilistic data interpretation is a straightforward problem involving conditional probability. A prior probability distribution is essential, and examples are given. In this small book (200 pages) the reader is led from the most basic concepts of mathematical probability all the way to parallel processing algorithms for Markov Chain Monte Carlo. Fortran source code (for eigenvalue analysis of finite discrete Markov Chains, for MCMC, and for nonlinear least squares) is included with the supplementary material for this book (available online).< Less
OTREC-RR-12-04 By Christopher Monsere, Mecit Cetin
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Vehicle re-identification matches vehicles crossing two different locations. Building on a previous study, we investigate different methods for re-identification and explore factors that impact... More > accuracy. Archived data from weigh-in-motion (WIM) stations in Oregon are used to develop, calibrate, and test vehicle re-identification algorithms. In addition to the Bayesian approach developed in the previous study, a neural network model is developed. The results show both methods to be effective while the Bayesian method results are more accurate. A comprehensive analysis employing the Bayesian algorithm matches vehicles that cross upstream and downstream pairs of WIM sites. Data from 14 different pairs of WIM sites are used to evaluate how factors such as distance, travel time variability, truck volumes, and sensors impact accuracy. The testing showed a large variation in accuracy, with sensor accuracy and volumes have the greatest impacts, while distance alone shows no significant impact.< Less
OTREC-RR-12-04 By Christopher Monsere, Mecit Cetin
Paperback: $6.70
Ships in 3-5 business days
Vehicle re-identification matches vehicles crossing two different locations. Building on a previous study, we investigate different methods for re-identification and explore factors that impact... More > accuracy. Archived data from weigh-in-motion (WIM) stations in Oregon are used to develop, calibrate, and test vehicle re-identification algorithms. In addition to the Bayesian approach developed in the previous study, a neural network model is developed. The results show both methods to be effective while the Bayesian method results are more accurate. A comprehensive analysis employing the Bayesian algorithm matches vehicles that cross upstream and downstream pairs of WIM sites. Data from 14 different pairs of WIM sites are used to evaluate how factors such as distance, travel time variability, truck volumes, and sensors impact accuracy. The testing showed a large variation in accuracy, with sensor accuracy and volumes have the greatest impacts, while distance alone shows no significant impact.< Less