The evolutionary forest algorithm
TL;DR: The evolutionary forest (EF) algorithm uses Monte Carlo methods to generate posterior distributions of population parameters to create a forest of genealogies based on an ensemble of histories, rather than a single tree.
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Abstract: Motivation: Gene genealogies offer a powerful context for inferences about the evolutionary process based on presently segregating DNA variation. In many cases, it is the distribution of population parameters, marginalized over the effectively infinite-dimensional tree space, that is of interest. Our evolutionary forest (EF) algorithm uses Monte Carlo methods to generate posterior distributions of population parameters. A novel feature is the updating of parameter values based on a probability measure defined on an ensemble of histories (a forest of genealogies), rather than a single tree.
Results: The EF algorithm generates samples from the correct marginal distribution of population parameters. Applied to actual data from closely related fruit fly species, it rapidly converged to posterior distributions that closely approximated the exact posteriors generated through massive computational effort. Applied to simulated data, it generated credible intervals that covered the actual parameter values in accordance with the nominal probabilities.
Availability: A C++ implementation of this method is freely accessible at http://www.isds.duke.edu/~scl13
Contact: scotland@stat.duke.edu
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Citations
The Multiset Sampler
TL;DR: The multiset sampler (MSS) as discussed by the authors is a new Metropolis-Hastings algorithm for drawing samples from a posterior distribution, which is designed to be effective when the posterior has the feature that the parameters can be divided into two sets, X, the parameters of interest and Y, the nuisance parameters.
The multiset EM algorithm
Weihong Huang,Yuguo Chen +1 more
TL;DR: It is demonstrated that the multiset EM algorithm can outperform the EM algorithm, especially when EM has difficulties in convergence and the E-step involves Monte Carlo approximation.
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The Generalized Multiset Sampler
Hang J. Kim,Steven N. MacEachern +1 more
TL;DR: The generalized formulation replaces the multiset with a K-tuple, which allows the algorithm to be used on unbounded parameter spaces, improves estimation, and sets up further extensions to adaptive MCMC techniques.
Suppression mechanism of the calcium sensitivity in Saccharomyces cerevisiae ptp2Δmsg5Δ double disruptant involves a novel HOG-independent function of Ssk2, transcription factor Msn2 and the protein kinase A component Bcy1
Walter A. Laviña,Hosein Shahsavarani,Abbas Saidi,Minetaka Sugiyama,Yoshinobu Kaneko,Satoshi Harashima +5 more
TL;DR: Genetic analysis revealed a novel, high osmolarity glycerol (HOG)-independent suppressor function of Ssk2 in relation to the Ptp2 and Msg5-mediated calcium signaling and identified 19 genes with distinct pattern of expression that are likely involved in the calcium sensitive phenotype of the ptp2Δmsg5Δ double disruptant.
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A theory for the multiset sampler
TL;DR: It is shown that the MSS converges to the target distribution faster as the multiset size increases, which explains the improvement in convergence rate for the M SS with large multisets sizes over the standard data augmentation scheme.
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Equation of state calculations by fast computing machines
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Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
Stuart Geman,Donald Geman +1 more
TL;DR: The analogy between images and statistical mechanics systems is made and the analogous operation under the posterior distribution yields the maximum a posteriori (MAP) estimate of the image given the degraded observations, creating a highly parallel ``relaxation'' algorithm for MAP estimation.
Monte Carlo Sampling Methods Using Markov Chains and Their Applications
TL;DR: A generalization of the sampling method introduced by Metropolis et al. as mentioned in this paper is presented along with an exposition of the relevant theory, techniques of application and methods and difficulties of assessing the error in Monte Carlo estimates.
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