A novel maximum likelihood solution to the problem of identifying parameters of a nonlinear model under missing observations is presented. An expectation maximization (EM) algorithm, which uses the expected value of the complete log-likelihood function including the missing observations, is developed. The expected value of the complete log-likelihood (E-step) in the EM algorithm is approximated...

IV jPhaseFit: the fitting module 5 IV-A Abstract Classes . . . . . . . . . . . . . . . . . . . . . . . . . 6 IV-B Concrete Classes: Maximum Likelihood Algorithms . . . . . . . 6 IV-B.1 General Phase-Type Distribution EM Algorithm [1] . . 6 IV-B.2 Hyper-Exponential Distribution EM Algorithm [2] . . . 7 IV-B.3 Hyper-Erlang Distribution EM Algorithm [3] . . . . . 7 IV-C Concrete Classes: Moment Ma...

A credal network is a graph-theoretic model that represents imprecision in joint probability distributions. An inference in a credal net aims at computing an interval for the probability of an interest event. The algorithms for inference in credal networks can be divided into exact and approximate. The selection of such an algorithm is based on a trade off that ponders how much time someone wan...

Optimisation of distribution parameters is a very common problem. There are many sorts of distributions which can be used to model environment processes, biological functions or graphical data. However, it is common that parameters of those distribution may be, partially or completely unknown. Mixture models composed of a few distributions are easier to solve. In such a case simple estimation m...

Inference is a key component in learning probabilistic models from partially observable data. When learning temporal models, each of the many inference phases requires a traversal over an entire long data sequence; furthermore, the data structures manipulated are exponentially large, making this process computationally expensive. In [2], we describe an approximate inference algorithm for monito...

We analyze the asymptotic convergence properties of a general class of EM type algorithms for es timating an unknown parameter via alternating estimation and maximization As examples this class includes ML EM penalized ML EM Green s OSL EM and many other approximate EM al gorithms A theorem is given which provides conditions for monotone convergence with respect to a given norm and speci es an ...

0167-8655/$ see front matter 2012 Elsevier B.V. A http://dx.doi.org/10.1016/j.patrec.2012.06.017 ⇑ Corresponding author. Tel.: +358 132517962. E-mail address: [email protected] (Q. Zhao). Expectation maximization (EM) algorithm is a popular way to estimate the parameters of Gaussian mixture models. Unfortunately, its performance highly depends on the initialization. We propose a random swap EM...

CONTENTS 1. Introduction 2. Image and blur models 3. Maximum likelihood (ML) parameter identification 3.1. Formulation 3.2. Constraints on the unknown parameters 4. ML parameter identification via the expectation-maximization (EM) algorithm 4.1. The EM algorithm in the linear Gaussian case 4.2. Choices of complete data 4.2.1. {x,y} as the complete data 4.2.2. {x,v} as the complete data Abstract...

As we all know, traditional electromagnetism mechanism (EM) algorithm has the disadvantage with low solution precision, lack of mining ability and easily falling into precocity. This paper proposes a new chaos electromagnetism mechanism algorithm combining chaotic mapping with limited storage QuasiNewton Method (EM-CMLSQN). Its main idea is that it adopts limit quasi-Newton operator to replace ...

Estimating parameters of mixture models is a typical application of the expectation maximization (EM) algorithm. For the family of multivariate exponential power (MEP) distributions, which is a generalization of the well known multivariate Gaussian distribution, we introduce an approximative EM algorithm, and a probabilistic variant called stochastic EM algorithm, which provides a significant s...