Herein, a new methodology using a 3D Electromagnetic (EM) simulator-based Support Vector Regression Machine (SVRM) models of base elements is presented for band-pass filter (BPF) design. SVRM models of elements, which are as fast as analytical equations and as accurate as a 3D EM simulator, are employed in a simple and efficient Cuckoo Search Algorithm (CSA) to optimize an ultrawideband (UWB) m...

In this paper, an improved expectation maximization (EM) algorithm called statistical histogram based expectation maximization (SHEM) algorithm is presented. The algorithm is put forward to overcome the drawback of standard EM algorithm, which is extremely computationally expensive for calculating the maximum likelihood (ML) parameters in the statistical segmentation. Combining the SHEM algorit...

Method of moments (MoM) has recently become an appealing alternative to standard iterative approaches like Expectation Maximization (EM) to learn latent variable models. In addition, MoM-based algorithms come with global convergence guarantees in the form of finite sample bounds. However, given enough computation time, by using restarts and heuristics to avoid local optima, iterative approaches...

In SPECT/PET, the maximum-likelihood expectation-maximization (ML-EM) algorithm is getting more attention as the speed of computers increases. This is because it can incorporate various physical aspects into the reconstruction process leading to a more accurate reconstruction than other analytical methods such as filtered-backprojection algorithms. However, the convergence rate of the ML-EM alg...

In this paper, the maximum likelihood estimation of a general two-level structural equation model with an unbalanced design is formulated as a missing data problem by treating the latent random vectors at the group level as hypothetical missing data. The commonly used EM algorithm is utilized to obtain the solution. Expressions for the E-step are derived and it is shown that the complex optimiz...

3 The Expectation-Maximization algorithm 7 3.1 Jointly-non-concave incomplete log-likelihood . . . . . . . . . . . 7 3.2 (Possibly) Concave complete data log-likelihood . . . . . . . . . . 8 3.3 The general EM derivation . . . . . . . . . . . . . . . . . . . . . 10 3.4 The E& M-steps . . . . . . . . . . . . . . . . . . . . . . . . . . 12 3.5 The EM algorithm . . . . . . . . . . . . . . . . . . ...

In this paper we propose algorithms for parameter estimation of fast-sampled homogeneous Markov chains observed in white Gaussian noise. Our algorithms are obtained by the robust discretization of stochastic differential equations involved in the estimation of continuous-time Hidden Markov Models (HMM’s) via the EM algorithm. We present two algorithms: The first is based on the robust discretiz...

EDML is a recently proposed algorithm for learning MAP parameters in Bayesian networks. In this paper, we present a number of new advances and insights on the EDML algorithm. First, we provide the multivalued extension of EDML, originally proposed for Bayesian networks over binary variables. Next, we identify a simplified characterization of EDML that further implies a simple fixed-point algori...

In this paper we investigate an efficient implementation of the Monte Carlo EM algorithm based on Quasi-Monte Carlo sampling. The Monte Carlo EM algorithm is a stochastic version of the deterministic EM (Expectation-Maximization) algorithm in which an intractable E-step is replaced by a Monte Carlo approximation. Quasi-Monte Carlo methods produce deterministic sequences of points that can signi...

The authors define ordered subset processing for standard algorithms (such as expectation maximization, EM) for image restoration from projections. Ordered subsets methods group projection data into an ordered sequence of subsets (or blocks). An iteration of ordered subsets EM is defined as a single pass through all the subsets, in each subset using the current estimate to initialize applicatio...