• SIMoNe

  • Referenced in 6 articles [sw06067]
  • simone: Statistical Inference for MOdular NEtworks (SIMoNe) , The R package simone implements the inference...
  • gergm

  • Referenced in 3 articles [sw21317]
  • using available methods for statistical inference with networks. The generalized exponential random graph model (GERGM...
  • bc3net

  • Referenced in 1 article [sw17708]
  • Simoes and Frank Emmert-Streib, Bagging Statistical Network Inference from Large-Scale Gene Expression Data...
  • Tuffy

  • Referenced in 10 articles [sw28901]
  • Markov Logic Network inference engine, and part of Felix. Markov Logic Networks (MLNs ... powerful framework that combines statistical and logical reasoning; they have been applied to many data ... data management techniques, Tuffy is an MLN inference engine that achieves scalability and orders...
  • Alchemy

  • Referenced in 11 articles [sw16040]
  • series of algorithms for statistical relational learning and probabilistic logic inference, based on the Markov ... Collective classification; Link prediction; Entity resolution; Social network modeling; Information extraction...
  • minet

  • Referenced in 7 articles [sw08432]
  • functions to infer mutual information networks from a dataset. Once fed with a microarray dataset ... package returns a network where nodes denote genes, edges model statistical dependencies between genes ... weight of an edge quantifies the statistical evidence of a specific (e.g transcriptional) gene ... well as four different inference methods, namely relevance networks, ARACNE, CLR and MRNET. Also...
  • netgwas

  • Referenced in 3 articles [sw21512]
  • netgwas: An R Package for Network-Based Genome-Wide Association Studies. Graphical models provide powerful ... tools to model and make the statistical inference regarding complex relationships among variables in multivariate ... widely used in statistics and machine learning particularly to analyze biological networks. In this paper ... 2017b), and in inferring the conditional independence network for non-Gaussian, discrete, and mixed data...
  • LS-SVMlab

  • Referenced in 26 articles [sw07367]
  • been introduced within the context of statistical learning theory and structural risk minimization ... SVMs are closely related to regularization networks and Gaussian processes but additionally emphasize and exploit ... analysis and extensions to unsupervised learning, recurrent networks and control are available. Robustness, sparseness ... Bayesian framework with three levels of inference has been developed. LS-SVM based primal-dual...
  • FACTORIE

  • Referenced in 13 articles [sw08947]
  • model structure, inference, and learning. By combining the traditional, declarative, statistical semantics of factor graphs ... language. In experimental comparisons to Markov Logic Networks on joint segmentation and coreference, we find...
  • latentnet

  • Referenced in 16 articles [sw10550]
  • evaluate statistical latent position and cluster models for networks. Hoff, Raftery, and Handcock (2002) suggested ... approach to modeling networks based on positing the existence of an latent space of characteristics ... Tantrum (2007). The package implements Bayesian inference for the models based on an Markov chain...
  • Graph_sampler

  • Referenced in 1 article [sw20652]
  • Bayesian networks (BNs) are widely used graphical models usable to draw statistical inference about Directed ... fast free C language software for structural inference on BNs. Graph_sampler uses a fully ... data and prior information about the network structure are considered. This new software can handle...
  • PANFIS

  • Referenced in 6 articles [sw13735]
  • novel algorithm, namely parsimonious network based on fuzzy inference system (PANFIS), is to this ... stitched up and expelled by virtue of statistical contributions of the fuzzy rules and injected...
  • RHugin

  • Referenced in 5 articles [sw16347]
  • building and making inference from Bayesian belief networks. The RHugin package provides a suite ... thus be used to build Bayesian belief networks, enter and propagate evidence, and to retrieve ... would like to take advantage of the statistical and programatic capabilities of R. Please note...
  • graphsim

  • Referenced in 1 article [sw33833]
  • package graphsim: Simulate Expression Data from ’igraph’ Networks. Functions to develop simulated continuous data ... correlations. Here we present a versatile statistical framework to simulate correlated gene expression data from ... statistical model of gene expression. For example methods to infer biological pathways and gene regulatory ... networks from gene expression data can be tested on simulated datasets using this framework. This...
  • HCC-Vis

  • Referenced in 2 articles [sw30710]
  • Coherence-based time series clustering for statistical inference and visualization of brain connectivity. We develop ... procedure for characterizing connectivity in a network by clustering nodes or groups of channels that...
  • KReator

  • Referenced in 3 articles [sw06946]
  • programming (or statistical relational learning) aims at applying probabilistic methods of inference and learning ... probabilistic methods like Bayes Nets and Markov Networks on relational settings. Only few developers provide ... area of probabilistic inductive logic programming and statistical relational learning. Currently, KReator implements Bayesian logic...
  • ARTMAP

  • Referenced in 5 articles [sw03013]
  • modify the fuzzy ARTMAP (FA) neural network (NN) using the Bayesian framework in order ... using Bayes’ decision theory for learning and inference, and 5) em- ploying the probabilistic association ... with respect to classification accuracy, sensitivity to statistical overlapping, learning curves, expected loss, and category...
  • matLeap

  • Referenced in 1 article [sw16549]
  • suitable for Bayesian inference. Background: Species abundance distributions in chemical reaction network models cannot usually ... leaping, and iii) provides summary statistics necessary for Bayesian inference. Results: We provide a Matlab...
  • ThiNet

  • Referenced in 3 articles [sw34568]
  • compress CNN models in both training and inference stages. We focus on the filter level ... method does not change the original network structure, thus it can be perfectly supported ... need to prune filters based on statistics information computed from its next layer...
  • MMG

  • Referenced in 4 articles [sw29328]
  • conditions. Popular approaches involve using t-test statistics, based on modelling the data as arising ... usually related through a complex (weighted) network of interactions, and often the more pertinent question ... networks and pathways. The method can easily incorporate information about weights in the network ... generalized to directed networks. We propose an efficient sampling strategy to infer posterior probabilities...