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Titlebook: Case Studies in Bayesian Statistics; Volume V Constantine Gatsonis,Robert E. Kass,Mike West Conference proceedings 2002 Springer Science+Bu

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https://doi.org/10.1007/978-1-4020-9716-4 prevalent and hip fractures are the most debilitating. BMD has been shown to predict fractures, and the rates of bone growth and loss with age have been identified as key determinants of osteoporosis in old age. To develop therapies for osteoporosis, we need to better understand the pattern of bone
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https://doi.org/10.1007/978-1-4039-1446-0complete Markov chain Monte Carlo (MCMC) methods, such as the Gibbs sampler, can be used to sample genotypes at the marker and trait loci. The Markov chain that corresponds to the scalar Gibbs sampler may not work due to slow mixing. Further, the Gibbs chain may not be irreducible when sampling geno
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https://doi.org/10.1007/978-1-4302-0047-5ansforming the sensory inputs into Markov chains and then applies our new agglomerative clustering procedure to discover the most probable set of clusters describing the robot’s experiences. To increase efficiency, the method uses an entropy-based heuristic search strategy.
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https://doi.org/10.1007/978-1-4302-0047-5t the outcomes of the two trials from over 50 HIV clinicians. Their prior opinions are used here for design: the sample size for reaching consensus with high probability is calculated. Consensus is said to occur when all clinicians have posterior opinions which would lead to pre-scribing the same tr
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