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The generalized canonical ensemble and its universal equivalence with the microcanonical ensemble
Statistical mechanics model the equivalent subset energy generalized canonical
2014/12/25
This paper shows for a general class of statistical mechanical models that when the microcanonical and canonical ensembles are nonequivalent on a subset of values of the energy, there often exists a g...
Complete analysis of phase transitions and ensemble equivalence for the Curie-Weiss-Potts model
Structure rotating model Potts model porter model
2014/12/25
Using the theory of large deviations, we analyze the phase transition structure of the Curie–Weiss–Potts spin model, which is a mean-field approximation to the nearest-neighbor Potts model. It is equi...
Metastability within the generalized canonical ensemble
The generalized canonical ensemble microcanonical ensemble metastable
2014/12/25
We discuss a property of our recently introduced generalized canonical ensemble [M. Costeniuc, R.S. Ellis, H. Touchette, B. Turkington, The generalized canonical ensemble and its universal equivalence...
Global Optimization, the Gaussian Ensemble, and Universal Ensemble Equivalence
Unconstrained problem global optimization statistical mechanics the equivalent theory of convex function
2014/12/25
Given a constrained minimization problem, under what conditions does there exist a related, unconstrained problem having the same minimum points? This basic question in global optimization motivates t...
Generalized canonical ensembles and ensemble equivalence
Boltzmann continuous function generalized canonical ensemble and physics
2014/12/25
This paper is a companion piece to our previous work [J. Stat. Phys. 119, 1283 (2005)], which introduced a generalized canonical ensemble obtained by multiplying the usual Boltzmann weight factor e...
Covariance inflation in the ensemble Kalman filter: a residual nudging perspective and some implications
Covariance inflation ensemble Kalman filter residual nudging perspective some implications
2013/6/17
This note examines the influence of covariance inflation on the distance between the measured observation and the simulated (or predicted) observation with respect to the state estimate. In order for ...
Ensemble Copula Coupling as a Multivariate Discrete Copula Approach
multivariate discrete copula stochastic array Sklar’s theorem statistical ensemble postprocessing ensemble copula coupling
2013/6/14
In probability and statistics, copulas play important roles theoretically as well as to address a wide range of problems in various application areas. In this paper, we introduce the concept of multiv...
Semi-supervised Clustering Ensemble by Voting
clustering ensembles semi supervised clustering consensus function ensemble generation.
2012/9/18
Clustering ensemble is one of the most recent advances in unsupervised learning. It aims to combine the clustering results obtained using different algorithms or from different runs of ...
Ensemble Clustering with Logic Rules
ensemble learning clustering, biological annotation logic rule random projection
2012/9/19
In this article, the logic rule ensembles approach to supervised learning is applied to the unsupervised or semi-supervised clustering. Logic rules which were obtained by combining simple conjunctive ...
Bridging the ensemble Kalman and particle filter
Bridging the ensemble Kalman particle filter
2012/9/17
In many applications of Monte Carlo nonlinear filtering, the propagation step is com-putationally expensive, and hence, the sample size is limited. With small sample sizes, the update step becomes cru...
Proportionate vs disproportionate distribution of wealth of two individuals in a tempered Paretian ensemble
Pareto law Paretian ensemble Truncated wealth distribution
2011/7/7
We study the distribution P(\omega) of the random variable \omega = x_1/(x_1 + x_2), where x_1 and x_2 are the wealths of two individuals selected at random from the same tempered Paretian ensemble ch...
Data Driven Computing by the Morphing Fast Fourier Transform Ensemble Kalman Filter in Epidemic Spread Simulations
Data Driven Computing Morphing Fast Fourier Transform Ensemble Kalman Filter Epidemic Spread Simulations
2010/3/11
The FFT EnKF data assimilation method is proposed and applied to a stochastic
cell simulation of an epidemic, based on the S-I-R spread model. The FFT EnKF
combines spatial statistics and ensemble f...
On the Convergence of the Ensemble Kalman Filter
Exchangeable random variables Monte-Carlo methods dataassimilation theoretical analysis asymptotics EnKF filtering
2010/3/17
Convergence of the ensemble Kalman filter in the limit for large ensembles
to the Kalman filter is proved. In each step of the filter, convergence of
the ensemble sample covariance follows from a we...
Morphing Ensemble Kalman Filters
Ensemble Kalman Filters image processing registration mapping
2010/4/29
A new type of ensemble filter is proposed, which combines an ensemble Kalman filter (EnKF)
with the ideas of morphing and registration from image processing. This results in filters
suitable for non...