Crossed random effects models can simultaneously take into account both fixed and of the subjects stimuli when observations are nested within combinations stimuli. Unfortunately, maximum likelihood estimation (MLE) restricted (REML) often encounter convergence problems, which in turn lead researchers to fit simpler that yield invalid statistical inferences. On other hand, if structure is too si...

Supplementary Methods 6 MAPPING METHOD 6 General theory behind likelihood maximization 7 MLE for expected degrees κ 7 MLE for angular coordinates θ 8 MLE kernels 8 First MLE wrapper 8 Algorithm 1 9 Second MLE wrapper 9 Algorithm 2 10 Parameter estimation and finite size effects 10 Estimating γ 10 Estimating N and k̄ 11 Estimating β 12 DEALING WITH NEW-COMING ASs 12 SENSITIVITY TO MISSING LINKS 1...

A number of problems in quantum state and system identification are addressed. Specifically, it is shown that the maximum likelihood estimation (MLE) approach, already known to apply to quantum state tomography, is also applicable to quantum process tomography (estimating the Kraus operator sum representation (OSR)), Hamiltonian parameter estimation, and the related problems of state and proces...

The performance of speech recognition system will be significantly deteriorated because of the mismatches between training and testing conditions. This paper addresses the problem and proposes an algorithm to adapt the mean and covariance of HMM simultaneously within the minimum classification error linear regression (MCELR) framework. Rather than estimating the transformation parameters using ...

Generative latent-variable models are important for natural language processing due to their capability of providing compact representations of data. As conventional maximum likelihood estimation (MLE) is prone to focus on explaining irrelevant but common correlations in data, we apply maximum reconstruction estimation (MRE) to learning generative latent-variable models alternatively, which aim...

Bernoulli HMMs (BHMMs) have been successfully applied to handwritten text recognition (HTR) tasks such as continuous and isolated handwritten words. BHMMs belong to the generative model family and, hence, are usually trained by (joint) maximum likelihood estimation (MLE) by means of the Baum-Welch algorithm. Despite the good properties of the MLE criterion, there are better training criteria su...

This tutorial describes a parameter estimation technique that is little-known in social sciences, namely maximum a posteriori estimation. This technique can be used in conjunction with prior knowledge to improve maximum likelihood estimation of the best-fitting parameters of a data set. The estimates are based on the mode of the posterior distribution of a Bayesian analysis. The relationship be...

The behavior of maximum likelihood estimates (MLEs) and the likelihood ratio statistic in a family of problems involving pointwise nonparametric estimation of a monotone function is studied. This class of problems differs radically from the usual parametric or semiparametric situations in that the MLE of the monotone function at a point converges to the truth at rate n (slower than the usual √ ...

Although the maximum likelihood estimation (MLE) for uncertain discrete models has long been an academic interest, it yet to be proposed in literature. Thus, this study proposes MLE framework of uncertainty theory, such as logistic regression model. We also generalize by Lio and Liu obtain non-linear continuous models, Box-Cox Our methods provide a useful tool making inferences regarding data t...

A low-complexity blind timing algorithm is proposed to estimate timing offset in OFDM systems when multiple symbols are received (the timing offset estimation is independent of the frequency offset one). Though the maximum-likelihood estimation (MLE) using two or three symbols is good in offset estimation, its performance can be significantly improved by including more symbols in our previous w...