An Interest Rate Adjusting Method with Bayesian Estimation in Social Lending
PRIMA '08 Proceedings of the 11th Pacific Rim International Conference on Multi-Agents: Intelligent Agents and Multi-Agent Systems
Generative model-based metasearch for data fusion in information retrieval
Proceedings of the 9th ACM/IEEE-CS joint conference on Digital libraries
Efficient identification of assembly neurons within massively parallel spike trains
Computational Intelligence and Neuroscience - Special issue on signal processing for neural spike trains
Exploring a Bayesian and linear approach to requirements traceability
Information and Software Technology
Approximate Recall Confidence Intervals
ACM Transactions on Information Systems (TOIS)
Bayesian analysis of GUHA hypotheses
Journal of Intelligent Information Systems
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The Introduction to Bayesian Statistics (2nd Edition) presents Bayes theorem, the estimation of unknown parameters, the determination of confidence regions and the derivation of tests of hypotheses for the unknown parameters, in a manner that is simple, intuitive and easy to comprehend. The methods are applied to linear models, in models for a robust estimation, for prediction and filtering and in models for estimating variance components and covariance components. Regularization of inverse problems and pattern recognition are also covered while Bayesian networks serve for reaching decisions in systems with uncertainties. If analytical solutions cannot be derived, numerical algorithms are presented such as the Monte Carlo integration and Markov Chain Monte Carlo methods.