Sentence examples for maximum likelihood network from inspiring English sources

We not only estimate a single maximum likelihood network, but compute a posterior distribution over networks, using a novel variant of the expectation propagation method.

A maximum-likelihood network history inference method has been applied to the problem of predicting regulatory interactions in present-day networks (Zhang and Moret, 2008, 2010).

Even when analyzed with different topology reconstruction methods like maximum parsimony and maximum likelihood, the network in Fig.  2 remained strongly supported, which suggests that a majority of the SNP haplotypes that will be identified in the future will cluster within the families defined with the BSNP in this study.

We used the R package bnlearn (Scutari, 2010) to calculate the maximum likelihood of the networks and the Akaike Information Criterion AICC) score.

Based on standard techniques in Bayesian networks [ 90], we include all network structures within the top 95% of scores based on the maximum likelihood and build a common network that includes those interactions that occur in more than 50% of these network structures.

We study the problem of projecting a distribution onto (or finding a maximum likelihood distribution among) Markov networks of bounded tree-width.

The performance of different conventional and machine learning classifiers namely Malahanobis Distance (MD), Maximum Likelihood (ML), Artificial Neural Networks (ANNs), Support Vector Machines (SVMs) and Random Forests (RFs) was compared using the same datasets in the same IDL Interactive Data Languageenvironmentenvironment

To classify one such study area in West Africa we integrated the optical sensors Landsat Thematic Mapper (TM) and the Advanced Visible and Near Infrared Radiometer type 2 (AVNIR-2) with the Phased Arrayed L-band SAR (PALSAR) sensor, the latter two on-board the Advanced Land Observation Satellite (ALOS), using traditional Maximum Likelihood (MLC) and Neural Networks (NN) classifiers.

We also considered several measures designed specifically for networks (both maximum likelihood measures [27] and extensions of a bipartite measure to networks [28]), but each of these requires a rooted phylogenetic network, which only one of the methods we tested (SHB) provides, therefore precluding its use here.

Traditional classification algorithms, such as maximum likelihood classifier, back propagation neural network classifier, are also involved for a comparison purpose.

This ROC plot also shows robust performance of the alternating decision tree classifier compared with other similar studies which uses Bayesian network and maximum likelihood estimation.