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164
Simulating Normalized Constants: From Importance Sampling to Bridge Sampling to Path Sampling
 Statistical Science, 13, 163–185. COMPARISON OF METHODS FOR COMPUTING BAYES FACTORS 435
, 1998
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Cited by 153 (4 self)
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Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at
Information Geometry of the EM and em Algorithms for Neural Networks
 Neural Networks
, 1995
"... In order to realize an inputoutput relation given by noisecontaminated examples, it is effective to use a stochastic model of neural networks. A model network includes hidden units whose activation values are not specified nor observed. It is useful to estimate the hidden variables from the obs ..."
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Cited by 105 (9 self)
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In order to realize an inputoutput relation given by noisecontaminated examples, it is effective to use a stochastic model of neural networks. A model network includes hidden units whose activation values are not specified nor observed. It is useful to estimate the hidden variables from the observed or specified inputoutput data based on the stochastic model. Two algorithms, the EM  and emalgorithms, have so far been proposed for this purpose. The EMalgorithm is an iterative statistical technique of using the conditional expectation, and the emalgorithm is a geometrical one given by information geometry. The emalgorithm minimizes iteratively the KullbackLeibler divergence in the manifold of neural networks. These two algorithms are equivalent in most cases. The present paper gives a unified information geometrical framework for studying stochastic models of neural networks, by forcussing on the EM and em algorithms, and proves a condition which guarantees their equ...
Toward a method of selecting among computational models of cognition
 Psychological Review
, 2002
"... The question of how one should decide among competing explanations of data is at the heart of the scientific enterprise. Computational models of cognition are increasingly being advanced as explanations of behavior. The success of this line of inquiry depends on the development of robust methods to ..."
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Cited by 94 (6 self)
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The question of how one should decide among competing explanations of data is at the heart of the scientific enterprise. Computational models of cognition are increasingly being advanced as explanations of behavior. The success of this line of inquiry depends on the development of robust methods to guide the evaluation and selection of these models. This article introduces a method of selecting among mathematical models of cognition known as minimum description length, which provides an intuitive and theoretically wellgrounded understanding of why one model should be chosen. A central but elusive concept in model selection, complexity, can also be derived with the method. The adequacy of the method is demonstrated in 3 areas of cognitive modeling: psychophysics, information integration, and categorization. How should one choose among competing theoretical explanations of data? This question is at the heart of the scientific enterprise, regardless of whether verbal models are being tested in an experimental setting or computational models are being evaluated in simulations. A number of criteria have been proposed to assist in this endeavor, summarized nicely by Jacobs and Grainger
Diffusion Kernels on Statistical Manifolds
, 2004
"... A family of kernels for statistical learning is introduced that exploits the geometric structure of statistical models. The kernels are based on the heat equation on the Riemannian manifold defined by the Fisher information metric associated with a statistical family, and generalize the Gaussian ker ..."
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Cited by 92 (6 self)
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A family of kernels for statistical learning is introduced that exploits the geometric structure of statistical models. The kernels are based on the heat equation on the Riemannian manifold defined by the Fisher information metric associated with a statistical family, and generalize the Gaussian kernel of Euclidean space. As an important special case, kernels based on the geometry of multinomial families are derived, leading to kernelbased learning algorithms that apply naturally to discrete data. Bounds on covering numbers and Rademacher averages for the kernels are proved using bounds on the eigenvalues of the Laplacian on Riemannian manifolds. Experimental results are presented for document classification, for which the use of multinomial geometry is natural and well motivated, and improvements are obtained over the standard use of Gaussian or linear kernels, which have been the standard for text classification.
Information Geometry on Hierarchy of Probability Distributions
, 2001
"... An exponential family or mixture family of probability distributions has a natural hierarchical structure. This paper gives an “orthogonal” decomposition of such a system based on information geometry. A typical example is the decomposition of stochastic dependency among a number of random variables ..."
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Cited by 73 (6 self)
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An exponential family or mixture family of probability distributions has a natural hierarchical structure. This paper gives an “orthogonal” decomposition of such a system based on information geometry. A typical example is the decomposition of stochastic dependency among a number of random variables. In general, they have a complex structure of dependencies. Pairwise dependency is easily represented by correlation, but it is more difficult to measure effects of pure triplewise or higher order interactions (dependencies) among these variables. Stochastic dependency is decomposed quantitatively into an “orthogonal” sum of pairwise, triplewise, and further higher order dependencies. This gives a new invariant decomposition of joint entropy. This problem is important for extracting intrinsic interactions in firing patterns of an ensemble of neurons and for estimating its functional connections. The orthogonal decomposition is given in a wide class of hierarchical structures including both exponential and mixture families. As an example, we decompose the dependency in a higher order Markov chain into a sum of those in various lower order Markov chains.
Histograms of oriented optical flow and binetcauchy kernels on nonlinear dynamical systems for the recognition of human actions
 in In IEEE Conference on Computer Vision and Pattern Recognition (CVPR
, 2009
"... System theoretic approaches to action recognition model the dynamics of a scene with linear dynamical systems (LDSs) and perform classification using metrics on the space of LDSs, e.g. BinetCauchy kernels. However, such approaches are only applicable to time series data living in a Euclidean space, ..."
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Cited by 54 (5 self)
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System theoretic approaches to action recognition model the dynamics of a scene with linear dynamical systems (LDSs) and perform classification using metrics on the space of LDSs, e.g. BinetCauchy kernels. However, such approaches are only applicable to time series data living in a Euclidean space, e.g. joint trajectories extracted from motion capture data or feature point trajectories extracted from video. Much of the success of recent object recognition techniques relies on the use of more complex feature descriptors, such as SIFT descriptors or HOG descriptors, which are essentially histograms. Since histograms live in a nonEuclidean space, we can no longer model their temporal evolution with LDSs, nor can we classify them using a metric for LDSs. In this paper, we propose to represent each frame of a video using a histogram of oriented optical flow (HOOF) and to recognize human actions by classifying HOOF timeseries. For this purpose, we propose a generalization of the BinetCauchy kernels to nonlinear dynamical systems (NLDS) whose output lives in a nonEuclidean space, e.g. the space of histograms. This can be achieved by using kernels defined on the original nonEuclidean space, leading to a welldefined metric for NLDSs. We use these kernels for the classification of actions in video sequences using (HOOF) as the output of the NLDS. We evaluate our approach to recognition of human actions in several scenarios and achieve encouraging results. 1.
Bankruptcy Analysis with SelfOrganizing Maps in Learning Metrics
 IEEE Transactions on Neural Networks
, 2001
"... We introduce a method for deriving a metric, locally based on the Fisher information matrix, into the data space. A SelfOrganizing Map is computed in the new metric to explore financial statements of enterprises. The metric measures local distances in terms of changes in the distribution of an auxi ..."
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Cited by 48 (19 self)
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We introduce a method for deriving a metric, locally based on the Fisher information matrix, into the data space. A SelfOrganizing Map is computed in the new metric to explore financial statements of enterprises. The metric measures local distances in terms of changes in the distribution of an auxiliary random variable that reflects what is important in the data. In this paper the variable indicates bankruptcy within the next few years. The conditional density of the auxiliary variable is first estimated, and the change in the estimate resulting from local displacements in the primary data space is measured using the Fisher information matrix. When a SelfOrganizing Map is computed in the new metric it still visualizes the data space in a topologypreserving fashion, but represents the (local) directions in which the probability of bankruptcy changes the most.
Extended ZivZakai Lower Bound for Vector Parameter Estimation
 IEEE Trans. Inform. Theory
, 1997
"... The Bayesian ZivZakai bound on the mean square error (MSE) in estimating a uniformly distributed continuous random variable is extended for arbitrarily distributed continuous random vectors and for distortion functions other than MSE. The extended bound is evaluated for some representative problem ..."
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Cited by 28 (0 self)
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The Bayesian ZivZakai bound on the mean square error (MSE) in estimating a uniformly distributed continuous random variable is extended for arbitrarily distributed continuous random vectors and for distortion functions other than MSE. The extended bound is evaluated for some representative problems in timedelay and bearing estimation. The resulting bounds have simple closedform expressions, and closely predict the simulated performance of the maximumlikelihood estimator in all regions of operation. Index Terms Parameter estimation, performance bounds, mean square error. I. INTRODUCTION L OWER bounds on the minimum mean square error (MSE) in estimating a set of parameters from noisy observations are widely used for problems where the exact minimum MSE is difficult to evaluate. Such bounds provide the unbeatable performance of any estimator in terms of the MSE. They can be used to investigate fundamental limits of a parameter estimation problem, or as a baseline for assessing...