Florian Angeletti

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Large deviation, Phase transition

On the existence of a glass transition in a Random Energy Model,

F. Angeletti, E. Bertin, P. Abry, J. Phys. A, (2013), 315002,
doi:10.1088/1751-8113/46/31/315002, arxiv:1303.5555, BIB, PDF


We revisit the Random Energy Model by assuming that the energy of each configuration is given by the sum of Nindependent contributions (``local energies'') with finite variances,instead of directly assuming a Gaussian energy distribution with a variance proportional to N. Using the large deviation formalism,we find that the glass transition generically exists when local energies have a smooth distribution. In contrast,if the distribution of the local energies has a Dirac mass at the minimal energy,the glass transition may cease to exist. This property is illustrated on a simple example,in which local energies are equal to zero with a finite probability,and positive otherwise.

Matrix representation, Non-stationary signal, Random variable synthesis

Random vector and time series definition and synthesis from matrix product representations: From Statistical Physics to Hidden Markov Models,

F. Angeletti, E. Bertin, P. Abry, IEEE Transactions on Signal Processing, (2013), 5389-5400,
doi:10.1109/TSP.2013.2278510, arxiv:1203.4500, BIB, PDF


Inspired from modern out-of-equilibrium statistical physics models,a matrix product based framework is defined and studied,that permits the formal definition of random vectors and time series whose desired joint distributions are a priori prescribed. Its key feature consists of preserving the writing of the joint distribution as the simple product structure it has under independence,while inputing controlled dependencies amongst components: This is obtained by replacing the product of probability densities by a product of matrices of probability densities. It is first shown that this matrix product model can be remapped onto the framework of Hidden Markov Models. Second,combining this double perspective enables us both to study the statistical properties of this model in terms of marginal distributions and dependencies (a stationarity condition is notably devised) and to devise an efficient and accurate numerical synthesis procedure. A design procedure is also described that permits the tuning of model parameters to attain targeted statistical properties. Pedagogical well-chosen examples of times series and multivariate vectors aim at illustrating the power and versatility of the proposed approach and at showing how targeted statistical properties can actually be prescribed.

Matrix representation, Random variables sums

Statistics of sums of correlated variables described by a matrix product ansatz,

F. Angeletti, E. Bertin, P. Abry, European Physics Letters, (2013), 50009,
doi:10.1209/0295-5075/104/50009, arxiv:1304.5406, BIB, PDF


We determine the asymptotic distribution of the sum of correlated variables described by a matrix product ansatz with finite matrices,considering variables with finite variances. In cases when the correlation length is finite,the law of large numbers is obeyed,and the rescaled sum converges to a Gaussian distribution. In constrast,when correlation extends over system size,we observe either a breaking of the law of large numbers,with the onset of giant fluctuations,or a generalization of the central limit theorem with a family of nonstandard limit distributions. The corresponding distributions are found as mixtures of delta functions for the generalized law of large numbers,and as mixtures of Gaussian distributions for the generalized central limit theorem. Connections with statistical physics models are emphasized.