Relatedness between individuals is central to many studies in genetics and population biology. A variety of estimators have been developed to enable molecular marker data to quantify relatedness. Despite this, no effort has been given to characterize the traditional maximum-likelihood estimator in relation to the remainder. This article quantifies its statistical performance under a range of bi...

Maximum likelihood sequence estimation (MLSE) has been proposed in earlier literature to combat the effects of nonlinear dispersion in intensity modulation/direct detection (IM/DD) optical channels. In this paper, we develop a theory of the bit error rate (BER) of MLSE-based IM/DD receivers operating in the presence of nonlinear dispersion and amplified spontaneous emission (ASE) noise. We focu...

A new likelihood based AR approximation is given for ARMA models. The usual algorithms for the computation of the likelihood of an ARMA model require O(n) flops per function evaluation. Using our new approximation, an algorithm is developed which requires only O(1) flops in repeated likelihood evaluations. In most cases, the new algorithm gives results identical to or very close to the exact ma...

Methods for improving the basic kernel density estimator include variable locations, variable bandwidths (often called variable kernels) and variable weights. Currently these methods are implemented separately and via pilot estimation of variation functions derived from asymptotic considerations. In this paper, we propose a simple maximum likelihood procedure which allows (in its greatest gener...

This paper is concerned with the parameter estimation of a relatively general class of nonlinear dynamic systems. A Maximum Likelihood (ML) framework is employed in the interests of statistical efficiency, and it is illustrated how an Expectation Maximisation (EM) algorithm may be used to compute these ML estimates. An essential ingredient is the employment of so-called “particle smoothing” met...

Approximate Bayesian Computation (ABC) may be viewed as an analytic approximation of an intractable likelihood coupled with an elementary simulation step. Considering the first step as an explicit approximation of the likelihood allows, also, maximum-likelihood (or maximum-aposteriori) inference to be conducted, approximately, using essentially the same techniques. Such an approach is developed...

In this paper, I provide a tutorial exposition on maximum likelihood estimation (MLE). The intended audience of this tutorial are researchers who practice mathematical modeling of cognition but are unfamiliar with the estimation method. Unlike least-squares estimation which is primarily a descriptive tool, MLE is a preferred method of parameter estimation in statistics and is an indispensable t...

A novel blind equalisation scheme is developed based on maximum likelihood (ML) joint channel and data estimation. In this scheme, the joint ML optimisation is decomposed into a two-level optimisation loop. An e cient version of genetic algorithms (GAs), known as a micro GA, is employed at the upper level to identify the unknown channel model and the Viterbi algorithm (VA) is used at the lower ...

A blind adaptive scheme is proposed for joint maximum likelihood (ML) channel estimation and data detection of singleinput multiple-output (SIMO) systems. The joint ML optimisation over channel and data is decomposed into an iterative optimisation loop. An efficient global optimisation algorithm called the repeated weighted boosting search is employed at the upper level to optimally identify th...