Probabilistic_Bayesian_Modeling | Bayesian Modeling Scripts in Python using Stan
kandi X-RAY | Probabilistic_Bayesian_Modeling Summary
kandi X-RAY | Probabilistic_Bayesian_Modeling Summary
Probabilistic_Bayesian_Modeling is a Python library. Probabilistic_Bayesian_Modeling has no bugs, it has no vulnerabilities and it has low support. However Probabilistic_Bayesian_Modeling build file is not available. You can download it from GitHub.
Bayesian Modeling Scripts in Python using Stan, Edward, and PyMC3. Note compiled Stan files (.pkl) are ignored by git so if you clone this repository you have to compile the models in 'compiled_models.dir' in your python directory. Also data/ is set to be ignored in .gitignore. However, the code is setup such that all the data is outside of the github folder as of now. In the same location where you see the folder Probabilistic_Bayesian_Modeling make another folder called poc_data, within which there should be two subdirectories called indata.dir and outdata.dir for the input and output data, respectively. Place the pom_flux folder from Team Drive into indata.dir before running the script. Please make sure you have the latest version of PyStan. The earlier versions gave me segmentation fault errors for some chains. If Edward gives you problems with the computation module in pandas, update dask. If PyMC3 gives you problems with sampling the posterior, update Numpy to the latest version.
Bayesian Modeling Scripts in Python using Stan, Edward, and PyMC3. Note compiled Stan files (.pkl) are ignored by git so if you clone this repository you have to compile the models in 'compiled_models.dir' in your python directory. Also data/ is set to be ignored in .gitignore. However, the code is setup such that all the data is outside of the github folder as of now. In the same location where you see the folder Probabilistic_Bayesian_Modeling make another folder called poc_data, within which there should be two subdirectories called indata.dir and outdata.dir for the input and output data, respectively. Place the pom_flux folder from Team Drive into indata.dir before running the script. Please make sure you have the latest version of PyStan. The earlier versions gave me segmentation fault errors for some chains. If Edward gives you problems with the computation module in pandas, update dask. If PyMC3 gives you problems with sampling the posterior, update Numpy to the latest version.
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Probabilistic_Bayesian_Modeling has a low active ecosystem.
It has 1 star(s) with 0 fork(s). There are 3 watchers for this library.
It had no major release in the last 6 months.
Probabilistic_Bayesian_Modeling has no issues reported. There are no pull requests.
It has a neutral sentiment in the developer community.
The latest version of Probabilistic_Bayesian_Modeling is current.
Quality
Probabilistic_Bayesian_Modeling has no bugs reported.
Security
Probabilistic_Bayesian_Modeling has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
License
Probabilistic_Bayesian_Modeling does not have a standard license declared.
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Probabilistic_Bayesian_Modeling releases are not available. You will need to build from source code and install.
Probabilistic_Bayesian_Modeling has no build file. You will be need to create the build yourself to build the component from source.
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Probabilistic_Bayesian_Modeling Key Features
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Probabilistic_Bayesian_Modeling Examples and Code Snippets
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Install Probabilistic_Bayesian_Modeling
You can download it from GitHub.
You can use Probabilistic_Bayesian_Modeling like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
You can use Probabilistic_Bayesian_Modeling like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
Support
For any new features, suggestions and bugs create an issue on GitHub.
If you have any questions check and ask questions on community page Stack Overflow .
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