• bnlearn

  • Referenced in 78 articles [sw08265]
  • structure learning, parameter learning and inference. Bayesian network structure learning, parameter learning and inference. This ... RSMAX2) structure learning algorithms for both discrete and Gaussian networks, along with many score functions ... well as support for parameter estimation (maximum likelihood and Bayesian) and inference, conditional probability queries...
  • ARACNE

  • Referenced in 38 articles [sw17200]
  • Relevance Networks and Bayesian Networks. Application to the deconvolution of genetic networks in human ... mutual information on network reconstruction, and show that algorithms based on mutual information ranking ... more resilient to estimation errors. Conclusion: ARACNE shows promise in identifying direct transcriptional interactions...
  • DirectLiNGAM

  • Referenced in 23 articles [sw15504]
  • Equation Model. Structural equation models and Bayesian networks have been widely used to analyze causal ... network structure, which is not the case with conventional methods. However, existing estimation methods ... based on iterative search algorithms and may not converge to a correct solution...
  • MATEDA

  • Referenced in 5 articles [sw07769]
  • with estimation of distribution algorithms (EDAs) based on undirected graphical models and Bayesian networks...
  • BFL

  • Referenced in 1 article [sw15150]
  • inference in Dynamic Bayesian Networks, i.e., recursive information processing and estimation algorithms based on Bayes ... read the rest of this post.The Bayesian Filtering Library (BFL) [ref] provides an application independent ... inference in Dynamic Bayesian Networks, i.e., recursive information processing and estimation algorithms based on Bayes...
  • latentnet

  • Referenced in 21 articles [sw10550]
  • Handcock (2002) suggested an approach to modeling networks based on positing the existence ... chain Monte Carlo algorithm. It can also compute maximum likelihood estimates for the latent position ... clustering). It also estimates which cluster each actor belongs to. These estimates are probabilistic ... types of point estimates for the coefficients and positions: maximum likelihood estimate, posterior mean, posterior...
  • LS-SVMlab

  • Referenced in 26 articles [sw07367]
  • nonlinear classification, function estimation and density estimation which has also led to many other recent ... SVMs are closely related to regularization networks and Gaussian processes but additionally emphasize and exploit ... algorithms such as kernel Fisher discriminant analysis and extensions to unsupervised learning, recurrent networks ... into LS-SVMs where needed and a Bayesian framework with three levels of inference...
  • Data2Dynamics

  • Referenced in 12 articles [sw25272]
  • construct dynamical models of biochemical reaction networks for large datasets and complex experimental conditions ... perform efficient and reliable parameter estimation for model fitting. We present a modeling environment ... variety of parameter estimation algorithms as well as frequentist and Bayesian methods for uncertainty analysis...
  • ARTMAP

  • Referenced in 7 articles [sw03013]
  • fuzzy ARTMAP (FA) neural network (NN) using the Bayesian framework in order to improve ... reduce its category proliferation. The proposed algorithm, called Bayesian ARTMAP (BA), preserves the FA advantages ... predict the class. In addition, the BA estimates the class posterior probability and thereby enables...
  • BCDAG

  • Referenced in 1 article [sw42853]
  • package BCDAG: Bayesian Structure and Causal Learning of Gaussian Directed Graphs. A collection of functions ... causal networks and estimation of joint causal effects from observational Gaussian data. Main algorithm consists...
  • PSICOV

  • Referenced in 7 articles [sw17010]
  • long-standing problem has increased recently with algorithmic improvements and the rapid growth ... introduces the use of sparse inverse covariance estimation to the problem of protein contact prediction ... performing normalized mutual information approach and Bayesian networks. For 118 out of 150 targets...
  • ZhuSuan

  • Referenced in 1 article [sw27939]
  • neural networks and supervised tasks, ZhuSuan provides deep learning style primitives and algorithms for building ... probabilistic models and applying Bayesian inference. The supported inference algorithms include: Variational inference with programmable ... variational posteriors, various objectives and advanced gradient estimators (SGVB, REINFORCE, VIMCO, etc.). Importance sampling...
  • BLNN

  • Referenced in 1 article [sw32579]
  • predictive power of neural networks with a breadth of Bayesian sampling techniques for the first ... Turn (NUTS) sampling algorithms with dual averaging for posterior weight generation. A robust implementation ... hyper-parameters and optional re-estimation through the evidence procedure gives BLNN high predictive precision...
  • SuperNNova

  • Referenced in 1 article [sw42354]
  • SuperNNova: an open-source framework for Bayesian, Neural Network based supernova classification. We introduce SuperNNova ... deep neural networks. Our core algorithm is a recurrent neural network (RNN) that is trained ... pitfalls of machine learning algorithms. We show that commonly used algorithms suffer from poor calibration ... estimate the robustness of classifiers and cast the learning procedure under a Bayesian light, demonstrating...
  • CARlasso

  • Referenced in 1 article [sw39712]
  • package for the estimation of sparse microbial networks with predictors. Microbiome data analyses require statistical ... model for the inference of sparse microbial networks that represent both interactions among nodes ... addition, CARlasso 1) enforces sparsity in the network via LASSO; 2) allows for an adaptive ... computationally inexpensive through an efficient Gibbs sampling algorithm so it can equally handle small...
  • cpi

  • Referenced in 1 article [sw41086]
  • predictive impact (CPI), a consistent and unbiased estimator of the association between one or several ... with any valid knockoff sampler, supervised learning algorithm, and loss function ... model selection, extending traditional frequentist and Bayesian techniques to general supervised learning tasks ... method using various algorithms, including linear regression, neural networks, random forests, and support vector machines...
  • gwbench

  • Referenced in 0 articles [sw40501]
  • high computational cost of Bayesian parameter estimation methods which renders them less effective ... scientific assessment of gravitational waveforms, detectors, and networks of detectors, especially when determining their effects ... host of waveforms available in the LSC Algorithm Library. With the provided functionality, gwbench...
  • ANSYS

  • Referenced in 713 articles [sw00044]
  • ANSYS offers a comprehensive software suite that spans...
  • ATLAS

  • Referenced in 199 articles [sw00056]
  • This paper describes the Automatically Tuned Linear Algebra...
  • BARON

  • Referenced in 361 articles [sw00066]
  • BARON is a computational system for solving nonconvex...