作者：
Karandashev, IakovKryzhanovsky, BorisRAS
Sci Res Inst Syst Anal Ctr Opt Neural Technol Nakhimovskiy Prosp 36B1 Moscow 117218 Russia RUDN Univ
Peoples Friendship Univ Russia 6 Miklukho Maklaya St Moscow 117198 Russia
In the literature the most frequently cited data are quite contradictory, and there is no consensus on the global minimum value of 2D Edwards-Anderson (2D EA) Ising model. By means of computer simulations, with the he...
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ISBN:
(纸本)9783030202576;9783030202569
In the literature the most frequently cited data are quite contradictory, and there is no consensus on the global minimum value of 2D Edwards-Anderson (2D EA) Ising model. By means of computer simulations, with the help of exactpolynomial Schraudolph-Kamenetsky algorithm, we examined the global minimum depth in 2D EA-type models. We found a dependence of the global minimum depth on the dimension of the problem N and obtained its asymptotic value in the limit N -> infinity. We believe these evaluations can be further used for examining the behavior of 2D Bayesian models often used in machine learning and image processing.
We derive an exact and efficient Bayesian regression algorithm for piecewise constant functions of unknown segment number, boundary locations, and levels. The derivation works for any noise and segment level prior, e....
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We derive an exact and efficient Bayesian regression algorithm for piecewise constant functions of unknown segment number, boundary locations, and levels. The derivation works for any noise and segment level prior, e.g. Cauchy which can handle outliers. We derive simple but good estimates for the in-segment variance. We also propose a Bayesian regression curve as a better way of smoothing data without blurring boundaries. The Bayesian approach also allows straightforward determination of the evidence, break probabilities and error estimates, useful for model selection and significance and robustness studies. We discuss the performance on synthetic and real-world examples. Many possible extensions are discussed.
作者：
Berry, VGascuel, OLIRMM
Dept Informat Fondamentale & Applicat F-34392 Montpellier 5 France Univ Warwick
Dept Comp Sci Coventry CV4 7AL W Midlands England Univ St Etienne
EURISE Dept Math St Etienne France
We consider the problem of inferring the evolutionary tree of a set of n species. We propose a quartet reconstruction method which specifically produces trees whose edges have strong combinatorial evidence. Let Q be a...
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We consider the problem of inferring the evolutionary tree of a set of n species. We propose a quartet reconstruction method which specifically produces trees whose edges have strong combinatorial evidence. Let Q be a set of resolved quartets defined on the studied species, the method computes the unique maximum subset Q* of Q which is equivalent to a tree and outputs the corresponding tree as an estimate of the species' phylogeny. We use a characterization of the subset Q* due to Bandelt and Dress (Adv. Appl. Math. 7 (1986) 309-343) to provide an O(n(4)) incremental algorithm for this variant of the NP-hard quartet consistency problem. Moreover, when chosing the resolution of the quartets by the four-Point method (FPM) and considering the Cavender-Farris model of evolution, we show that the convergence rate of the Q* method is at worst polynomial when the maximum evolutive distance between two species is bounded. We complete these theoretical results by an experimental study on real and simulated data sets. The results show that (i) as expected, the strong combinatorial constraints it imposes on each edge leads the Q* method to propose very few incorrect edges;(ii) more surprisingly;the method infers trees with a relatively high degree of resolution. (C) 2000 Published by Elsevier Science B.V. All rights reserved.
作者：
Berry, VGascuel, OLIRMM
Dept Informat Fondamentale & Applicat F-34392 Montpellier 5 France Univ Warwick
Dept Comp Sci Coventry CV4 7AL W Midlands England Univ St Etienne
EURISE Dept Math St Etienne France
We consider the problem of inferring the evolutionary tree of a set of n species. We propose a quartet reconstruction method which specifically produces trees whose edges have strong combinatorial evidence. Let Q be a...
详细信息
ISBN:
(纸本)354063357X
We consider the problem of inferring the evolutionary tree of a set of n species. We propose a quartet reconstruction method which specifically produces trees whose edges have strong combinatorial evidence. Let Q be a set of resolved quartets defined on the studied species, the method computes the unique maximum subset Q* of Q which is equivalent to a tree and outputs the corresponding tree as an estimate of the species' phylogeny. We use a characterization of the subset Q* due to Bandelt and Dress (Adv. Appl. Math. 7 (1986) 309-343) to provide an O(n(4)) incremental algorithm for this variant of the NP-hard quartet consistency problem. Moreover, when chosing the resolution of the quartets by the four-Point method (FPM) and considering the Cavender-Farris model of evolution, we show that the convergence rate of the Q* method is at worst polynomial when the maximum evolutive distance between two species is bounded. We complete these theoretical results by an experimental study on real and simulated data sets. The results show that (i) as expected, the strong combinatorial constraints it imposes on each edge leads the Q* method to propose very few incorrect edges;(ii) more surprisingly;the method infers trees with a relatively high degree of resolution. (C) 2000 Published by Elsevier Science B.V. All rights reserved.
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