Algebraic statistical models

Many statistical models are algebraic in that they are defined in terms of polynomial constraints, or in terms of polynomial or rational parametrizations. The parameter spaces of such models are typically semi-algebraic subsets of the parameter space of a reference model with nice properties, such as for example a regular exponential family. This observation leads to the definition of an `algebraic exponential family'. This new definition provides a unified framework for the study of statistical models with algebraic structure. In this paper we review the ingredients to this definition and illustrate in examples how computational algebraic geometry can be used to solve problems arising in statistical inference in algebraic models

Fluctuations in statistical models

Proceedings of 4th International Workshop "Critical Point and Onset of Deconfinement", July 9-13, 2007, Darmstadt, Germany: The multiplicity fluctuations of hadrons are studied within the statistical hadron-resonance gas model in the large volume limit. The role of quantum statistics and resonance decay effects are discussed. The microscopic correlator method is used to enforce conservation of three charges - baryon number, electric charge, and strangeness - in the canonical ensemble. In addition, in the micro-canonical ensemble energy conservation is included. An analytical method is used to account for resonance decays. The multiplicity distributions and the scaled variances for negatively and positively charged hadrons are calculated for the sets of thermodynamical parameters along the chemical freeze-out line of central Pb+Pb (Au+Au) collisions from SIS to LHC energies. Predictions obtained within different statistical ensembles are compared with the preliminary NA49 experimental results on central Pb+Pb collisions in the SPS energy range. The measured fluctuations are significantly narrower than the Poisson ones and clearly favor expectations for the micro-canonical ensemble. Thus, this is a first observation of the recently predicted suppression of the multiplicity fluctuations in relativistic gases in the thermodynamical limit due to conservation laws

Statistical Models of Nuclear Fragmentation

A method is presented that allows exact calculations of fragment multiplicity distributions for a canonical ensemble of non-interacting clusters. Fragmentation properties are shown to depend on only a few parameters. Fragments are shown to be copiously produced above the transition temperature. At this transition temperature, the calculated multiplicity distributions broaden and become strongly super-Poissonian. This behavior is compared to predictions from a percolation model. A corresponding microcanonical formalism is also presented.Comment: 12 pages, 5 figure

Statistical Models on Spherical Geometries

We use a one-dimensional random walk on $D$-dimensional hyper-spheres to determine the critical behavior of statistical systems in hyper-spherical geometries. First, we demonstrate the properties of such a walk by studying the phase diagram of a percolation problem. We find a line of second and first order phase transitions separated by a tricritical point. Then, we analyze the adsorption-desorption transition for a polymer growing near the attractive boundary of a cylindrical cell membrane. We find that the fraction of adsorbed monomers on the boundary vanishes exponentially when the adsorption energy decreases towards its critical value.Comment: 8 pages, latex, 2 figures in p

Statistical models for market segmentation

It is an essential element of market research that customer preferences are considered and the heterogeneity of these preferences is recognized. By segmenting the market into homogeneous clusters the preferences of customers is addressed. Latent class methodology for conjoint analysis, proposed by Green (2000), is one of the several conjoint segmentation procedures that overcome the limitations of aggregate analysis and priori segmentation. This approach proposes the proportional odds model as a proper statistical model for ordinal categorical data in which the item attributes are included in the linear predictor. The likelihood is maximized through the EM algorithm. This paper considers two extensions of this methodology that incorporate individual characteristics into the models.peer-reviewe

Tropical Geometry of Statistical Models

This paper presents a unified mathematical framework for inference in graphical models, building on the observation that graphical models are algebraic varieties. From this geometric viewpoint, observations generated from a model are coordinates of a point in the variety, and the sum-product algorithm is an efficient tool for evaluating specific coordinates. The question addressed here is how the solutions to various inference problems depend on the model parameters. The proposed answer is expressed in terms of tropical algebraic geometry. A key role is played by the Newton polytope of a statistical model. Our results are applied to the hidden Markov model and to the general Markov model on a binary tree.Comment: 14 pages, 3 figures. Major revision. Applications now in companion paper, "Parametric Inference for Biological Sequence Analysis

Active Learning with Statistical Models

For many types of machine learning algorithms, one can compute the statistically `optimal' way to select training data. In this paper, we review how optimal data selection techniques have been used with feedforward neural networks. We then show how the same principles may be used to select data for two alternative, statistically-based learning architectures: mixtures of Gaussians and locally weighted regression. While the techniques for neural networks are computationally expensive and approximate, the techniques for mixtures of Gaussians and locally weighted regression are both efficient and accurate. Empirically, we observe that the optimality criterion sharply decreases the number of training examples the learner needs in order to achieve good performance.Comment: See http://www.jair.org/ for any accompanying file

Semi-Inclusive Distributions in Statistical Models

The semi-inclusive properties of the system of neutral and charged particles with net charge equal to zero are considered in the grand canonical, canonical and micro-canonical ensembles as well as in micro-canonical ensemble with scaling volume fluctuations. Distributions of neutral particle multiplicity and charged particle momentum are calculated as a function of the number of charged particles. Different statistical ensembles lead to qualitatively different dependencies. They are being compared with the corresponding experimental data on multi-hadron production in $p+p$ interactions at high energies.Comment: Two subsections are added: "Average multiplicities, fluctuations and correlations" and "Quantum statistics