Open AccessJournal Article
A gentle tutorial of the em algorithm and its application to parameter estimation for Gaussian mixture and hidden Markov models
Reads0
Chats0
TLDR
In this paper, the authors describe the EM algorithm for finding the parameters of a mixture of Gaussian densities and a hidden Markov model (HMM) for both discrete and Gaussian mixture observation models.Abstract:
We describe the maximum-likelihood parameter estimation problem and how the ExpectationMaximization (EM) algorithm can be used for its solution. We first describe the abstract form of the EM algorithm as it is often given in the literature. We then develop the EM parameter estimation procedure for two applications: 1) finding the parameters of a mixture of Gaussian densities, and 2) finding the parameters of a hidden Markov model (HMM) (i.e., the Baum-Welch algorithm) for both discrete and Gaussian mixture observation models. We derive the update equations in fairly explicit detail but we do not prove any convergence properties. We try to emphasize intuition rather than mathematical rigor.read more
Citations
More filters
Journal ArticleDOI
Adaptive Variational Method for Restoring Color Images with High Density Impulse Noise
TL;DR: Two models with variational framework of restoring color images with impulse noise are presented, Inspired by the expectation-maximization (EM) algorithm, and the superiority of the proposed models is that: the weighting functions can effectively detect the noise in the image; with the noise information, the proposed algorithm can automatically balance the regularity of the restored image and the fidelity term by updating the weighted functions and the control parameters.
Journal ArticleDOI
Continuous-Trait Probabilistic Model for Comparing Multi-species Functional Genomic Data
Yang Yang,Quanquan Gu,Yang Zhang,Takayo Sasaki,Julianna Crivello,Rachel J. O’Neill,David M. Gilbert,Jian Ma +7 more
TL;DR: In this paper, a phylogenetic hidden Markov Gaussian processes (Phylo-HMGP) model is proposed to simultaneously infer heterogeneous evolutionary states of functional genomic features in a genome-wide manner.
Proceedings ArticleDOI
Coupled hidden markov models for user activity in social networks
TL;DR: It is demonstrated that the coupled HMM explains and predicts the observed activity profile more accurately than a renewal process-based model or a conventional uncoupled HMM, provided that the observations are sufficiently long to ensure accurate model learning.
Proceedings ArticleDOI
A Probabilistic Ensemble Pruning Algorithm
Huanhuan Chen,Peter Tino,Xin Yao +2 more
TL;DR: A probabilistic ensemble pruning algorithm is introduced by choosing a set of "sparse" combination weights, most of which are zero, to prune the large ensemble by solving benchmark regression problems and benchmark classification problems to demonstrate the effectiveness of the method.
Journal ArticleDOI
Automatic design of an effective image filter based on an evolutionary algorithm for venous analysis
Koji Kashihara,Masashi Iwase +1 more
TL;DR: A novel filtering method based on the genetic algorithm with the expectation–maximization algorithm was proposed for the visualization of vein shapes and its effectiveness was evaluated by images acquired from a near-infrared (780 nm) camera.
References
More filters
Journal ArticleDOI
Maximum likelihood from incomplete data via the EM algorithm
Book
The Nature of Statistical Learning Theory
TL;DR: Setting of the learning problem consistency of learning processes bounds on the rate of convergence ofLearning processes controlling the generalization ability of learning process constructing learning algorithms what is important in learning theory?
Statistical learning theory
TL;DR: Presenting a method for determining the necessary and sufficient conditions for consistency of learning process, the author covers function estimates from small data pools, applying these estimations to real-life problems, and much more.
Book
The Fractal Geometry of Nature
TL;DR: This book is a blend of erudition, popularization, and exposition, and the illustrations include many superb examples of computer graphics that are works of art in their own right.