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General Information

  • About AMIDST
    • What is AMIDST?
    • Scalability
      • Multi-Core Scalablity using Java 8 Streams
      • Distributed Scalablity using Apache Flink
    • Related Software

Examples

  • Sparklink: Code Examples
    • Input/output
      • Reading data
      • Writing data
    • Parameter learning
  • Wekalink: using an AMIDST classifier in Weka
    • Prepare your project
    • Create the wrapper class
    • Testing the AMIDST classifier in Weka
  • Tutorial: Easy Machine Learning with Latent Variable Models in AMIDST
    • Setting up
    • Static Models
      • Learning and saving to disk
      • Learning from Flink
      • Inference
      • Custom static model
    • Dynamic Models
      • Inference
      • Custom dynamic model
  • Flinklink: Code Examples
    • Input/output
      • Reading data
      • Writing data
    • Parametric learning
      • Parallel Maximum Likelihood
      • Distributed Variational Message Passing
      • Distributed VI
      • Stochastic VI
    • Extensions and applications
      • Latent variable models with Flink
      • Concept drift detection
  • Dynamic Bayesian Networks: Code Examples
    • Data Streams
    • Dynamic Random Variables
    • Dynamic Bayesian networks
      • Creating Dynamic Bayesian networks
      • Creating Dynamic Bayesian Networks with Latent Variables
      • Modifying Dynamic Bayesian Networks
    • Sampling from Dynamic Bayesian Networks
    • Inference Algorithms for Dynamic Bayesian Networks
      • The Dynamic MAP Inference
      • The Dynamic Variational Message Passing
      • The Dynamic Importance Sampling
    • Dynamic Learning Algorithms
      • Maximum Likelihood for DBNs
      • Streaming Variational Bayes for DBNs
  • Bayesian Networks: Code Examples
    • Data Streams
    • Data Streams
    • Models
      • Creating BNs
      • Creating Bayesian networks with latent variables
      • Modifying Bayesian networks
    • Input/Output
      • I/O of data streams
      • I/O of BNs
    • Inference
      • The inference engine
    • Inference
      • Variational Message Passing
      • Importance Sampling
    • Learning Algorithms
      • Maximum Likelihood
      • Parallel Maximum Likelihood
      • Streaming Variational Bayes
      • Parallel Streaming Variational Bayes
    • Concept Drift Methods
      • Naive Bayes with Virtual Concept Drift Detection
    • HuginLink
      • Models conversion between AMiDST and Hugin
      • I/O of Bayesian Networks with Hugin net format
      • Invoking Hugin’s inference engine
      • Invoking Hugin’s Parallel TAN
    • MoaLink
      • AMIDST Classifiers from MOA
      • AMIDST Classifiers from MOA

First steps

  • Getting Started!
    • Quick start
    • Getting started in detail
  • Requirements for AMIDST Toolbox
    • For toolbox users
    • For AMIDST developers
  • Loading AMIDST dependencies from a remote maven repository
  • Installing a local AMIDST repository
  • Generating the packages for each module and for its dependencies

Contributing to AMIDST

  • Basic steps for contributing
    • Clone the repository
    • Create a new branch from develop
    • Modify the code and upload your changes
    • Merge the new branch with develop

Other

  • JavaDoc
InferPy
  • Docs »
  • Search
  • Edit on GitHub

© Copyright 2017, Andrés R. Masegosa, Rafael Cabañas Revision cd3b227e.

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