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Maximum Likelihood from Incomplete Data Via the EM Algorithm

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A New Approach to Linear Filtering and Prediction Problems

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Neural networks for pattern recognition

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Statistical Analysis With Missing Data

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Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images

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Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference

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Least squares quantization in PCM

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Pattern classification and scene analysis

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Self-organized formation of topologically correct feature maps

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An Information-Maximization Approach to Blind Separation and Blind Deconvolution

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Independent component analysis, A new concept?

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Error bounds for convolutional codes and an asymptotically optimum decoding algorithm

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New Results in Linear Filtering and Prediction Theory

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Turbulence and the dynamics of coherent structures. I. Coherent structures

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An introduction to hidden Markov models

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A Rapidly Convergent Descent Method for Minimization

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A Maximization Technique Occurring in the Statistical Analysis of Probabilistic Functions of Markov Chains

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Theory and Practice of Recursive Identification

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Local Computations with Probabilities on Graphical Structures and Their Application to Expert Systems

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Adaptive filtering prediction and control

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Statistical Inference for Probabilistic Functions of Finite State Markov Chains

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Hierarchical Mixtures of Experts and the EM Algorithm

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A New Learning Algorithm for Blind Signal Separation

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Graphical Models in Applied Multivariate Statistics

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Mixtures of Probabilistic Principal Component Analyzers

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Maximum likelihood estimates of linear dynamic systems

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Hidden Markov Models: Estimation and Control

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AN APPROACH TO TIME SERIES SMOOTHING AND FORECASTING USING THE EM ALGORITHM

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An inequality and associated maximization technique in statistical estimation of probabilistic functions of a Markov process

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Time Series Prediction: Forecasting The Future And Understanding The Past

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GTM: The Generative Topographic Mapping

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Neural networks and principal component analysis: Learning from examples without local minima

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Autoencoders, Minimum Description Length and Helmholtz Free Energy

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An inequality with applications to statistical estimation for probabilistic functions of Markov processes and to a model for ecology

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Exponentiated Gradient versus Gradient Descent for Linear Predictors

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Some contributions to maximum likelihood factor analysis

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10.2 Maximum Likelihood from Incomplete Data

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EM Algorithms for PCA and SPCA

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The EM algorithm for mixtures of factor analyzers

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Supervised learning from incomplete data via an EM approach

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An Introduction to Latent Variable Models

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Parameter estimation for linear dynamical systems

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Variational Learning for Switching State-Space Models

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Clustering Sequences with Hidden Markov Models

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Modeling the manifolds of images of handwritten digits

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Regression with Input-dependent Noise: A Gaussian Process Treatment

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Probabilistic Independence Networks for Hidden Markov Probability Models

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Generative models for discovering sparse distributed representations

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ML estimation of a stochastic linear system with the EM algorithm and its application to speech recognition

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Solutions to the linear smoothing problem

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Dynamic Linear Models with Switching

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Maximum likelihood for blind separation and deconvolution of noisy signals using mixture models

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On tempo tracking: Tempogram Representation and Kalman filtering

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Maximum Likelihood Blind Source Separation: A Context-Sensitive Generalization of ICA

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GTM: A principled alternative to the Self-Organizing Map

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Maximum Likelihood Competitive Learning

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Blind Source Separation and Deconvolution: The Dynamic Component Analysis Algorithm

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Inferring the eigenvalues of covariance matrices from limited, noisy data

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Training Neural Networks with Deficient Data

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Recognizing Handwritten Digits Using Mixtures of Linear Models

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Update Rules for Parameter Estimation in Bayesian Networks

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Blind Source Separation and Deconvolution: The Dynamic Component Analysis Algorithm

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Dynamic Linear Models With Switching

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Learning interaction dynamics with coupled hidden Markov models

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Resolution-Based Complexity Control for Gaussian Mixture Models

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Modeling Acoustic Correlations by Factor Analysis

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