.. _sphx_glr_auto_tutorials_Bayesian_Methods:

.. _bayesian_methods_examples:

Bayesian Methods
-----------------

Tutorials for Bayesian approaches to uncertainty estimation.



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    <div class="sphx-glr-thumbcontainer" tooltip="In this tutorial, we will train a variational inference Bayesian Neural Network (viBNN) LeNet classifier on the MNIST dataset.">

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  .. image:: /auto_tutorials/Bayesian_Methods/images/thumb/sphx_glr_tutorial_bayesian_thumb.png
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  :ref:`sphx_glr_auto_tutorials_Bayesian_Methods_tutorial_bayesian.py`

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      <div class="sphx-glr-thumbnail-title">Training a Bayesian Neural Network in 20 seconds</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="In this tutorial, we train a LeNet classifier on the MNIST dataset using Monte-Carlo Batch Normalization (MCBN), a post-hoc Bayesian approximation method.">

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  .. image:: /auto_tutorials/Bayesian_Methods/images/thumb/sphx_glr_tutorial_mc_batch_norm_thumb.png
    :alt:

  :ref:`sphx_glr_auto_tutorials_Bayesian_Methods_tutorial_mc_batch_norm.py`

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      <div class="sphx-glr-thumbnail-title">Training a LeNet with Monte Carlo Batch Normalization</div>
    </div>


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    <div class="sphx-glr-thumbcontainer" tooltip="In this tutorial, we will train a LeNet classifier on the MNIST dataset using Monte-Carlo Dropout (MC Dropout), a computationally efficient Bayesian approximation method. To estimate the predictive mean and uncertainty (variance), we perform multiple forward passes through the network with dropout layers enabled in train mode.">

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  .. image:: /auto_tutorials/Bayesian_Methods/images/thumb/sphx_glr_tutorial_mc_dropout_thumb.png
    :alt:

  :ref:`sphx_glr_auto_tutorials_Bayesian_Methods_tutorial_mc_dropout.py`

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      <div class="sphx-glr-thumbnail-title">Training a LeNet with Monte-Carlo Dropout</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="This tutorial demonstrates how to train a segmentation model on the MUAD dataset using TorchUncertainty. MUAD is a synthetic dataset designed for evaluating autonomous driving under diverse uncertainties. It includes 10,413 images across training, validation, and test sets, featuring adverse weather, lighting conditions, and out-of-distribution (OOD) objects. The dataset supports tasks like semantic segmentation, depth estimation, and object detection.">

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  .. image:: /auto_tutorials/Bayesian_Methods/images/thumb/sphx_glr_tutorial_muad_mc_drop_thumb.png
    :alt:

  :ref:`sphx_glr_auto_tutorials_Bayesian_Methods_tutorial_muad_mc_drop.py`

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      <div class="sphx-glr-thumbnail-title">Monte Carlo Dropout for Semantic Segmentation on MUAD</div>
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.. toctree::
   :hidden:

   /auto_tutorials/Bayesian_Methods/tutorial_bayesian
   /auto_tutorials/Bayesian_Methods/tutorial_mc_batch_norm
   /auto_tutorials/Bayesian_Methods/tutorial_mc_dropout
   /auto_tutorials/Bayesian_Methods/tutorial_muad_mc_drop