A maximum likelihood estimation method for a mixture of shifted binomial distributions
نویسندگان
چکیده
منابع مشابه
Penalized Maximum Likelihood Estimation for Normal Mixture Distributions
Mixture models form the essential basis of data clustering within a statistical framework. Here, the estimation of the parameters of a mixture of Gaussian densities is considered. In this particular context, it is well known that the maximum likelihood approach is statistically ill posed, i.e. the likelihood function is not bounded above, because of singularities at the boundary of the paramete...
متن کاملPenalized Maximum Likelihood Estimation for Univariate Normal Mixture Distributions
Due to singularities of the likelihood function, the maximum likelihood approach for the estimation of the parameters of normal mixture models is an acknowledged ill posed optimization problem. Ill posedness is solved by penalizing the likelihood function. In the Bayesian framework, it amounts to incorporating an inverted gamma prior in the likelihood function. A penalized version of the EM alg...
متن کاملA comparison of algorithms for maximum likelihood estimation of Spatial GLM models
In spatial generalized linear mixed models, spatial correlation is assumed by adding normal latent variables to the model. In these models because of the non-Gaussian spatial response and the presence of latent variables the likelihood function cannot usually be given in a closed form, thus the maximum likelihood approach is very challenging. The main purpose of this paper is to introduce two n...
متن کاملMaximum Likelihood Estimation of Feature-Based Distributions
Motivated by recent work in phonotactic learning (Hayes and Wilson 2008, Albright 2009), this paper shows how to define feature-based probability distributions whose parameters can be provably efficiently estimated. The main idea is that these distributions are defined as a product of simpler distributions (cf. Ghahramani and Jordan 1997). One advantage of this framework is it draws attention t...
متن کاملFast exact maximum likelihood estimation for mixture of language model
Language modeling is an effective and theoretically attractive probabilistic framework for text information retrieval. The basic idea of this approach is to estimate a language model of a given document (or document set), and then do retrieval or classification based on this model. A common language modeling approach assumes the data D is generated from a mixture of several language models. The...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of the Korean Data and Information Science Society
سال: 2014
ISSN: 1598-9402
DOI: 10.7465/jkdi.2014.25.1.255