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Encog is a machine learning framework available for Java and .Net. [1] Encog supports different learning algorithms such as Bayesian Networks, Hidden Markov Models and Support Vector Machines. However, its main strength lies in its neural network algorithms. Encog contains classes to create a wide variety of networks, as well as support classes ...
Bayesian program learning has potential applications voice recognition and synthesis, image recognition and natural language processing. It employs the principles of compositionality (building abstract representations from parts), causality (building complexity from parts) and learning to learn (using previously recognized concepts to ease the ...
Bayesian program synthesis differs both in that the constraints are probabilistic and the output is itself a distribution over programs that can be further refined. Additionally, Bayesian program synthesis can be contrasted to the work on Bayesian program learning, where probabilistic program components are hand-written, pre-trained on data ...
JASP – A free software alternative to IBM SPSS Statistics with additional option for Bayesian methods; JMulTi – For econometric analysis, specialised in univariate and multivariate time series analysis; Just another Gibbs sampler (JAGS) – a program for analyzing Bayesian hierarchical models using Markov chain Monte Carlo developed by ...
Sequential Bayesian filtering is the extension of the Bayesian estimation for the case when the observed value changes in time. It is a method to estimate the real value of an observed variable that evolves in time. There are several variations: filtering when estimating the current value given past and current observations, smoothing
Automatically learning the graph structure of a Bayesian network (BN) is a challenge pursued within machine learning. The basic idea goes back to a recovery algorithm developed by Rebane and Pearl [ 7 ] and rests on the distinction between the three possible patterns allowed in a 3-node DAG:
In natural language processing, latent Dirichlet allocation (LDA) is a Bayesian network (and, therefore, a generative statistical model) for modeling automatically extracted topics in textual corpora. The LDA is an example of a Bayesian topic model. In this, observations (e.g., words) are collected into documents, and each word's presence is ...
Excalibur: Java inversion of control framework including containers and components; Falcon: data governance engine; Forrest: documentation framework based upon Cocoon; Giraph: scalable Graph Processing System; Hama: Hama is an efficient and scalable general-purpose BSP computing engine; Harmony: Java SE 5 and 6 runtime and development kit