A broadly applicable algorithm for computing maximum likelihood estimates from incomplete data is presented at various levels of generality. Theory showing the monotone behaviour of the likelihood and convergence of the algorithm is derived. Many examples are sketched, including missing value

Abstract-Speech recognition is formulated as a problem of maximum likelihood decoding. This formulation requires statistical models of the speech production process. In this paper, we describe a number of sta-tistical models for use in speech recognition. We give special attention to determining

We derive the form of the best non-linear functions for performing independent component analysis (ICA) by maximum likelihood estimation. We show that both the form of nonlinearity and the relative gradient update equations for likelihood maximization naturally generalize to the complex case

registration based on general image acquisition models, we show that the search for an optimal measure can be cast into a maximum likelihood estimation problem. We then derive similarity measures corresponding to different modeling assumptions and retrieve some well-known measures (correlation coefficient

, the function that takes the votes as input and produces the outcome that maximizes the likelihood of these votes as output. We will first review some of the work on the maximum likelihood approach to voting. Most of this work supposes that, conditional on the correct outcome, votes are independent. In reality

This paper presents a novel framework, based on maximum likelihood, for training models to recognise simple spatial-motion events, such as those described by the verbs pick up, put down, push, pull, drop, and throw, and classifying novel observations into previously trained classes. The model

We considerthe problem of selecting a subset of alternatives given noisy evaluations of the relative strength of different alternatives. We wish to select a k-subset (for a given k) that provides a maximum likelihood estimate for one of several objectives, e.g., containing the strongest alternative

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Multisensory response enhancement (MRE) is the augmentation of the response of a neuron to sensory input of one modality by simultaneous input from another modality. The maximum likelihood (ML) model presented here modi es the Bayesian model for MRE (Anastasio et al.) by incorporating a

Abstract—Scaffolding is the important stage of genome as-sembly consisting of orienting and ordering contigs based on read pairs. We present a scalable scaffolding algorithm that finds most likely contig orientation using integer linear program solved by a non-serial dynamic programming approach