.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "examples\1-tutorials\getting_started.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code .. rst-class:: sphx-glr-example-title .. _sphx_glr_examples_1-tutorials_getting_started.py: Getting Started with Visual Scripting in Bayesian Inference ========================================================== `visual_bayesic` leverages the power of visual scripting to simplify and enhance the Bayesian inference process. This "Getting Started" guide demonstrates how the combination of graphical representations and Python scripts can make defining and running Bayesian models more intuitive and user-friendly. Let's dive in! .. GENERATED FROM PYTHON SOURCE LINES 11-29 .. code-block:: default import os import sys from importlib import import_module from visual_bayesic.runner import display_graph, execute_model, import_model # Determine the base path if '__file__' in globals(): base_path = os.path.dirname(__file__) else: base_path = os.getcwd() # Adjust the Python module search path to include the parent directory sys.path.append(os.path.join(base_path, '..')) normal_likelihood = import_module(f"tutorial") .. GENERATED FROM PYTHON SOURCE LINES 30-33 The visual scripting graph below represents the Bayesian inference process. Each node corresponds to a step or component, and the connections depict the flow of data and dependencies. This graphical view provides an intuitive way to understand the structure and flow of the Bayesian model. .. GENERATED FROM PYTHON SOURCE LINES 33-36 .. code-block:: default display_graph(normal_likelihood) .. image-sg:: /examples/1-tutorials/images/sphx_glr_getting_started_001.png :alt: getting started :srcset: /examples/1-tutorials/images/sphx_glr_getting_started_001.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 37-41 .. code-block:: default # Execute the model execute_model(normal_likelihood) .. rst-class:: sphx-glr-horizontal * .. image-sg:: /examples/1-tutorials/images/sphx_glr_getting_started_002.png :alt: getting started :srcset: /examples/1-tutorials/images/sphx_glr_getting_started_002.png :class: sphx-glr-multi-img * .. image-sg:: /examples/1-tutorials/images/sphx_glr_getting_started_003.png :alt: Likelihood mean , Likelihood mean , Likelihood std, Likelihood std :srcset: /examples/1-tutorials/images/sphx_glr_getting_started_003.png :class: sphx-glr-multi-img * .. image-sg:: /examples/1-tutorials/images/sphx_glr_getting_started_004.png :alt: Likelihood mean , Likelihood mean , Likelihood std, Likelihood std :srcset: /examples/1-tutorials/images/sphx_glr_getting_started_004.png :class: sphx-glr-multi-img * .. image-sg:: /examples/1-tutorials/images/sphx_glr_getting_started_005.png :alt: Likelihood mean , Likelihood mean , Likelihood std, Likelihood std :srcset: /examples/1-tutorials/images/sphx_glr_getting_started_005.png :class: sphx-glr-multi-img * .. image-sg:: /examples/1-tutorials/images/sphx_glr_getting_started_006.png :alt: Likelihood mean , Likelihood std :srcset: /examples/1-tutorials/images/sphx_glr_getting_started_006.png :class: sphx-glr-multi-img * .. image-sg:: /examples/1-tutorials/images/sphx_glr_getting_started_007.png :alt: getting started :srcset: /examples/1-tutorials/images/sphx_glr_getting_started_007.png :class: sphx-glr-multi-img .. rst-class:: sphx-glr-script-out .. code-block:: none Executing: NormalSampler Executing: GammaSampler Executing: NormalSampler Executing: PyroModel Executing: VisualizeModelGraph Executing: FullInference Warmup: 0%| | 0/2000 [00:00, ?it/s] Warmup: 0%| | 10/2000 [00:00, 95.88it/s, step size=7.28e-02, acc. prob=0.754] Warmup: 1%|▏ | 25/2000 [00:00, 108.99it/s, step size=1.92e-02, acc. prob=0.761] Warmup: 2%|▏ | 36/2000 [00:00, 93.40it/s, step size=8.41e-03, acc. prob=0.762] Warmup: 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Sample: 100%|██████████| 2000/2000 [00:06, 294.10it/s, step size=6.56e-01, acc. prob=0.779] Executing: ArvizObject C:\Users\MigueldelaVarga\PycharmProjects\VisualBayesic\venv\lib\site-packages\arviz\data\io_pyro.py:157: UserWarning: Could not get vectorized trace, log_likelihood group will be omitted. Check your model vectorization or set log_likelihood=False warnings.warn( Executing: PlotPrior Executing: PlotTrace Executing: PlotDensity Executing: PlotNormalLikelihoodJoy Setting Backend To: AvailableBackends.numpy .. GENERATED FROM PYTHON SOURCE LINES 42-47 Conclusion ---------- Through this example, we experienced the unique approach of `visual_bayesic` that harnesses the power of visual scripting to simplify Bayesian inference. This intuitive blend of graphics and code empowers users to effectively define, understand, and execute Bayesian models. .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 9.874 seconds) .. _sphx_glr_download_examples_1-tutorials_getting_started.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: getting_started.py ` .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: getting_started.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_