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‎docs/pages/Continuous Data.html‎

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bnlearn
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<p class="caption" role="heading"><span class="caption-text">Quickstart</span></p>
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<li class="toctree-l1"><a class="reference internal" href="Introduction.html">Introduction</a></li>
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<li class="toctree-l1"><a class="reference internal" href="Quickstart.html">Installation</a></li>
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<p class="caption" role="heading"><span class="caption-text">Installation</span></p>
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<p class="caption" role="heading"><span class="caption-text">Discretizing</span></p>
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<li class="toctree-l1"><a class="reference internal" href="Discretizing.html">Discretizing</a></li>
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<li class="toctree-l1"><a class="reference internal" href="Discretizing.html#manual-discretization">Manual Discretization</a></li>
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<li class="toctree-l1"><a class="reference internal" href="Discretizing.html#automatic-discretization-probability-density">Automatic Discretization: Probability Density</a></li>
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<li class="toctree-l1"><a class="reference internal" href="Discretizing.html#automatic-discretization-principled-bayesian">Automatic Discretization: Principled Bayesian</a></li>
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<p class="caption" role="heading"><span class="caption-text">Structure learning</span></p>
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<li class="toctree-l1"><a class="reference internal" href="Structure%20learning.html">Causation</a></li>
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<li class="toctree-l1"><a class="reference internal" href="Structure%20learning.html#exhaustivesearch">Exhaustivesearch</a></li>
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<li class="toctree-l1"><a class="reference internal" href="Structure%20learning.html#hillclimbsearch">Hillclimbsearch</a></li>
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<li class="toctree-l1"><a class="reference internal" href="Structure%20learning.html#tree-augmented-naive-bayes-tan">Tree-augmented Naive Bayes (TAN)</a></li>
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<li class="toctree-l1"><a class="reference internal" href="Structure%20learning.html#naivebayes">NaiveBayes</a></li>
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<li class="toctree-l1"><a class="reference internal" href="Structure%20learning.html#constraint-based">Constraint-based</a></li>
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<p class="caption" role="heading"><span class="caption-text">Parameter learning</span></p>
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<li class="toctree-l1"><a class="reference internal" href="Parameter%20learning.html">Parameter learning</a></li>
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<li class="toctree-l1"><a class="reference internal" href="Parameter%20learning.html#conditional-probability-distributions-cpd">Conditional Probability Distributions (CPD)</a></li>
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<p class="caption" role="heading"><span class="caption-text">Inference</span></p>
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<p class="caption" role="heading"><span class="caption-text">Continuous Data</span></p>
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<li class="toctree-l1"><a class="reference internal" href="Continuous%20Data.html">Modelling Continuous Datasets</a></li>
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<p class="caption" role="heading"><span class="caption-text">Predict</span></p>
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<p class="caption" role="heading"><span class="caption-text">Synthetic Data</span></p>
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<li class="toctree-l1"><a class="reference internal" href="Sampling.html">Forward Sampling</a></li>
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<li class="toctree-l1"><a class="reference internal" href="Sampling.html#gibbs-sampling">Gibbs Sampling</a></li>
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<p class="caption" role="heading"><span class="caption-text">Plot</span></p>
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<p class="caption" role="heading"><span class="caption-text">Other functionalities</span></p>
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<li class="toctree-l1"><a class="reference internal" href="independence_test.html">Independence test</a></li>
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<li class="toctree-l1"><a class="reference internal" href="Create%20DAG.html">Directed Acyclic Graphs</a></li>
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<li class="toctree-l1"><a class="reference internal" href="impute.html">Impute</a></li>
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<li class="toctree-l1"><a class="reference internal" href="Example%20Datasets.html">DataFrames</a></li>
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<li class="toctree-l1"><a class="reference internal" href="Example%20Datasets.html#import-dag-bif">Import DAG/BIF</a></li>
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<li class="toctree-l1"><a class="reference internal" href="Example%20Datasets.html#export-dag-bif">Export DAG/BIF</a></li>
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<li class="toctree-l1"><a class="reference internal" href="Structure_scores.html">Structure Scores</a></li>
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<li class="toctree-l1"><a class="reference internal" href="saving%20and%20loading.html">Saving and Loading</a></li>
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<p class="caption" role="heading"><span class="caption-text">Examples</span></p>
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<li class="toctree-l1"><a class="reference internal" href="Examples.html#working-with-raw-data">Working with Raw Data</a></li>
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<li class="toctree-l1"><a class="reference internal" href="Examples.html#create-a-bayesian-network-learn-its-parameters-from-data-and-perform-the-inference">Create a Bayesian Network, learn its parameters from data and perform the inference</a></li>
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<p class="caption" role="heading"><span class="caption-text">Use Cases</span></p>
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<li class="toctree-l1"><a class="reference internal" href="UseCases.html">Titanic Dataset Analysis</a></li>
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<li class="toctree-l1"><a class="reference internal" href="UseCases.html#medical-domain-analysis">Medical Domain Analysis</a></li>
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<li class="toctree-l1"><a class="reference internal" href="UseCases.html#use-case-continuous-datasets">Use Case Continuous Datasets</a></li>
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<p class="caption" role="heading"><span class="caption-text">Parameters and attributes</span></p>
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<h1>Licence<a class="headerlink" href="#licence" title="Link to this heading"></a></h1>
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<p>MIT License</p>
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<p>Copyright (c) 2020 Erdogan Taskesen
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bnlearn - Python package for learning the graphical structure of Bayesian networks, parameter learning, inference and sampling methods.</p>
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<p>Permission is hereby granted, free of charge, to any person obtaining a copy
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