Statistical Physics for Sparse Statistical Inference
Author: Ayaka Sakata
Publisher:
Published: 2026-03
Total Pages: 0
ISBN-13: 9780750349987
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Author: Ayaka Sakata
Publisher:
Published: 2026-03
Total Pages: 0
ISBN-13: 9780750349987
DOWNLOAD EBOOKAuthor: Hinnerk Christian Schmidt
Publisher:
Published: 2018
Total Pages: 0
ISBN-13:
DOWNLOAD EBOOKDatasets come in a variety of forms and from a broad range of different applications. Typically, the observed data is noisy or in some other way subject to randomness. The recent developments in machine learning have revived the need for exact theoretical limits of probabilistic methods that recover information from noisy data. In this thesis we are concerned with the following two questions: what is the asymptotically best achievable performance? And how can this performance be achieved, i.e., what is the optimal algorithmic strategy? The answer depends on the properties of the data. The problems in this thesis can all be represented as probabilistic graphical models. The generative process of the data determines the structure of the underlying graphical model. The structures considered here are either sparse random graphs or dense (fully connected) models. The above questions can be studied in a probabilistic framework, which leads to an average (or typical) case answer. Such a probabilistic formulation is natural to statistical physics and leads to a formal analogy with problems in disordered systems. In turn, this permits to harvest the methods developed in the study of disordered systems, to attack constraint satisfaction and statistical inference problems. The formal analogy can be exploited as follows. The optimal performance analysis is directly related to the structure of the extrema of the macroscopic free energy. The algorithmic aspects follow from the minimization of the microscopic free energy (that is, the Bethe free energy in this work) which is closely related to message passing algorithms. This thesis is divided into four contributions. First, a statistical physics investigation of the circular coloring problem is carried out that reveals several distinct features. Second, new rigorous upper bounds on the size of minimal contagious sets in random graphs, with bounded maximum degree, are obtained. Third, the phase diagram of the dense Dawid-Skene model is derived by mapping the problem onto low-rank matrix factorization. The associated approximate message passing algorithm is evaluated on real-world data. Finally, the Bayes optimal denoising mean square error is derived for a restricted class of extensive rank matrix estimation problems.
Author: Florent Krzakala
Publisher: Oxford University Press
Published: 2016
Total Pages: 319
ISBN-13: 0198743734
DOWNLOAD EBOOKIn the last decade, there have been an increasing convergence of interest and methods between theoretical physics and fields as diverse as probability, machine learning, optimization and compressed sensing. In particular, many theoretical and applied works in statistical physics and computer science have relied on the use of message passing algorithms and their connection to statistical physics of spin glasses. The aim of this book, especially adapted to PhD students, post-docs, and young researchers, is to present the background necessary for entering this fast developing field.
Author: P. Grassberger
Publisher: Springer
Published: 1994-03-31
Total Pages: 372
ISBN-13:
DOWNLOAD EBOOKProceedings of the NATO Advanced Study Institute, Cargèse (Corsica), France, August 31--September 12, 1992
Author: Jean Barbier (physicien).)
Publisher:
Published: 2015
Total Pages: 0
ISBN-13:
DOWNLOAD EBOOKThis thesis is interested in the application of statistical physics methods and inference to signal processing and coding theory, more precisely, to sparse linear estimation problems. The main tools are essentially the graphical models and the approximate message-passing algorithm together with the cavity method (referred as the state evolution analysis in the signal processing context) for its theoretical analysis. We will also use the replica method of statistical physics of disordered systems which allows to associate to the studied problems a cost function referred as the potential of free entropy in physics. It allows to predict the different phases of typical complexity of the problem as a function of external parameters such as the noise level or the number of measurements one has about the signal: the inference can be typically easy, hard or impossible. We will see that the hard phase corresponds to a regime of coexistence of the actual solution together with another unwanted solution of the message passing equations. In this phase, it represents a metastable state which is not the true equilibrium solution. This phenomenon can be linked to supercooled water blocked in the liquid state below its freezing critical temperature. Thanks to this understanding of blocking phenomenon of the algorithm, we will use a method that allows to overcome the metastability mimicing the strategy adopted by nature itself for supercooled water: the nucleation and spatial coupling. In supercooled water, a weak localized perturbation is enough to create a crystal nucleus that will propagate in all the medium thanks to the physical couplings between closeby atoms. The same process will help the algorithm to find the signal, thanks to the introduction of a nucleus containing local information about the signal. It will then spread as a "reconstruction wave" similar to the crystal in the water. After an introduction to statistical inference and sparse linear estimation, we will introduce the necessary tools. Then we will move to applications of these notions. They will be divided into two parts. The signal processing part will focus essentially on the compressed sensing problem where we seek to infer a sparse signal from a small number of linear projections of it that can be noisy. We will study in details the influence of structured operators instead of purely random ones used originally in compressed sensing. These allow a substantial gain in computational complexity and necessary memory allocation, which are necessary conditions in order to work with very large signals. We will see that the combined use of such operators with spatial coupling allows the implementation of an highly optimized algorithm able to reach near to optimal performances. We will also study the algorithm behavior for reconstruction of approximately sparse signals, a fundamental question for the application of compressed sensing to real life problems. A direct application will be studied via the reconstruction of images measured by fluorescence microscopy. The reconstruction of "natural" images will be considered as well. In coding theory, we will look at the message-passing decoding performances for two distincts real noisy channel models. A first scheme where the signal to infer will be the noise itself will be presented. The second one, the sparse superposition codes for the additive white Gaussian noise channel is the first example of error correction scheme directly interpreted as a structured compressed sensing problem. Here we will apply all the tools developed in this thesis for finally obtaining a very promising decoder that allows to decode at very high transmission rates, very close of the fundamental channel limit.
Author: Deborah G. Mayo
Publisher: Cambridge University Press
Published: 2018-09-20
Total Pages: 503
ISBN-13: 1108563309
DOWNLOAD EBOOKMounting failures of replication in social and biological sciences give a new urgency to critically appraising proposed reforms. This book pulls back the cover on disagreements between experts charged with restoring integrity to science. It denies two pervasive views of the role of probability in inference: to assign degrees of belief, and to control error rates in a long run. If statistical consumers are unaware of assumptions behind rival evidence reforms, they can't scrutinize the consequences that affect them (in personalized medicine, psychology, etc.). The book sets sail with a simple tool: if little has been done to rule out flaws in inferring a claim, then it has not passed a severe test. Many methods advocated by data experts do not stand up to severe scrutiny and are in tension with successful strategies for blocking or accounting for cherry picking and selective reporting. Through a series of excursions and exhibits, the philosophy and history of inductive inference come alive. Philosophical tools are put to work to solve problems about science and pseudoscience, induction and falsification.
Author: A. Isihara
Publisher: Academic Press
Published: 2013-09-11
Total Pages: 455
ISBN-13: 1483274101
DOWNLOAD EBOOKStatistical Physics provides an introduction to the basic principles of statistical mechanics. Statistical mechanics is one of the fundamental branches of theoretical physics and chemistry, and deals with many systems such as gases, liquids, solids, and even molecules which have many atoms. The book consists of three parts. Part I gives the principles, with elementary applications to noninteracting systems. It begins with kinetic theory and discusses classical and quantum systems in equilibrium and nonequilibrium. In Part II, classical statistical mechanics is developed for interacting systems in equilibrium and nonequilibrium. Finally, in Part III, quantum statistics is presented to an extent which enables the reader to proceed to advanced many-body theories. This book is written for a one-year graduate course in statistical mechanics or a half-year course followed by a half-year course on related subjects, such as special topics and applications or elementary many-body theories. Efforts are made such that discussions of each subject start with an elementary level and end at an advanced level.
Author: A.M. Guenault
Publisher: Springer Science & Business Media
Published: 1995-02-28
Total Pages: 242
ISBN-13: 0412579200
DOWNLOAD EBOOKIn this revised and enlarged second edition, Tony Guénault provides a clear and refreshingly readable introduction to statistical physics. The treatment itself is self-contained and concentrates on an understanding of the physical ideas, without requiring a high level of mathematical sophistication. The book adopts a straightforward quantum approach to statistical averaging from the outset. The initial part of the book is geared towards explaining the equilibrium properties of a simple isolated assembly of particles. The treatment of gases gives full coverage to Maxwell-Boltzmann, Fermi-Dirac and Bose-Einstein statistics.
Author: Josef Honerkamp
Publisher: Springer Science & Business Media
Published: 2013-03-09
Total Pages: 519
ISBN-13: 3662047632
DOWNLOAD EBOOKThe book is divided into two parts. The first part looks at the modeling of statistical systems before moving on to an analysis of these systems. This second edition contains new material on: estimators based on a probability distribution for the parameters; identification of stochastic models from observations; and statistical tests and classification methods.
Author: Shrinivas Kudekar
Publisher:
Published: 2009
Total Pages: 200
ISBN-13:
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