By Sylvia Frühwirth-Schnatter, Angela Bitto, Gregor Kastner, Alexandra Posekany
Unique contributions from BAYSM 2014 researchers conceal contemporary advancements in Bayesian statistics
Includes educational in addition to commercial examine and purposes of Bayesian statistics
Incorporates enter from popular plenary teachers and senior discussants
The moment Bayesian younger Statisticians assembly (BAYSM 2014) and the learn provided right here facilitate connections between researchers utilizing Bayesian records by way of delivering a discussion board for the improvement and alternate of rules. WU Vienna collage of commercial and Economics hosted BAYSM 2014 from September 18th to the nineteenth. The counsel of well known plenary teachers and senior discussants is a serious a part of the assembly and this quantity, which follows booklet of contributions from BAYSM 2013. The meeting's medical application mirrored the range of fields within which Bayesian equipment are at the moment hired or might be brought sooner or later. 3 wonderful keynote lectures through Chris Holmes (University of Oxford), Christian Robert (Université Paris-Dauphine), and Mike West (Duke University), have been complemented by means of 24 plenary talks protecting the most important issues Dynamic types, functions, Bayesian Nonparametrics, Biostatistics, Bayesian tools in Economics, and versions and strategies, in addition to a full of life poster consultation with 30 contributions. chosen contributions were drawn from the convention for this e-book. All contributions during this quantity are peer-reviewed and proportion unique examine in Bayesian computation, software, and concept.
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Additional info for Bayesian Statistics from Methods to Models and Applications: Research from BAYSM 2014
Stat. Soc. Ser. : Statistical Analysis of Finite Mixture Distributions. : Diffusion-based EM algorithm for distributed estimation of Gaussian mixtures in wireless sensor networks. Sensors 11(6), 6297–316 (2011) Chapter 4 Jeffreys’ Priors for Mixture Estimation Clara Grazian and Christian P. Robert Abstract Mixture models may be a useful and flexible tool to describe data with a complicated structure, for instance characterized by multimodality or asymmetry. The literature about Bayesian analysis of mixture models is huge, nevertheless an “objective” Bayesian approach for these models is not widespread, because it is a well-established fact that one needs to be careful in using improper prior distributions, since the posterior distribution may not be proper, yet noninformative priors are often improper.
In magenta the tests with a bigger a-priori variance for Kn , in green the tests corresponding to a relatively small variance a-priori, in blue the intermediate ones. (a) A7, A8, A9. (b) B7, B8, B9. (c) C7, C8, C9 I. 4 Fig. 4 (a) Traceplots and histogram for variable ε in the second test case. e. 10 Fig. 01, respectively. We report for brevity only results for (α , β ) = (1, 10); however, we point out that some mixing problems in the chain for ε arise, when increasing the a-priori variance. 4b shows that ε moves a posteriori towards smaller values with respect to the prior information.
A way to accelerate the Metropolis–Hastings algorithm used to approximate the posterior distribution derived from the Jeffreys’ prior is the Delayed Acceptance algorithm proposed by Banterle et al.  (Algorithm 1). 44 C. P. Robert Algorithm 1 Delayed Acceptance algorithm Choose the initial values w0 , μ 0 , σ 0 for i in 1 : N do Propose w prop , μ prop , σ prop ∼ K(·, ·, ·|w(i−1), μ (i−1) , σ (i−1)) Simulate u1 ∼ Uni f (0, 1) and u2 ∼ Uni f (0, 1) l(w prop ,μ prop ,σ prop ) K(w(i−1) ,μ (i−1) ,σ (i−1) |w prop ,μ prop ,σ prop ) then l(w(i−1) ,μ (i−1) ,σ (i−1) ) K(w prop ,μ prop ,σ prop |w(i−1) ,μ (i−1) ,σ (i−1) ) π J (w prop ,μ prop ,σ prop ) (i) (i) (i) if u2 < π J (w(i−1) ,μ (i−1) ,σ (i−1) ) then Set (w , μ , σ ) = (w prop , μ prop , σ prop ) else (w(i) , μ (i) , σ (i) ) = (w(i−1) , μ (i−1) , σ (i−1)) if u1 < end if else Set (w(i) , μ (i) , σ (i) ) = (w(i−1) , μ (i−1) , σ (i−1)) end if end for This exact algorithm allows to compute the Jeffreys’ prior (the more expensive part of the posterior distribution) only when a first accept/reject step depending on the likelihood ratio (less costly) leads to acceptance and reduces the computational time by about 80 % (from an average of about 113 h with standard Metropolis– Hastings algorithm to an average of about 32 h with the Delayed Acceptance version) for 106 simulations for a three-component Gaussian mixture model with all parameters unknown, with a decrease of the acceptance rates from about 35 % with the standard Metropolis algorithm to about 20 % with the Delayed Acceptance.