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Montgomery County Community College – Pathify
Home – Montgomery County Community College
Montgomery County Community College, an affordable, quality coeducational community college with campuses in Blue Bell and Pottstown, Pennsylvania, offers a wide variety of credit and non-credit courses and two year programs to the Montgomery County community.
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MCCC Portal – MCCC Knowledge Base – Montgomery County Community College
https://kb.mc3.edu/category/information-technology/mccc-portal/64
MCCC Portal Articles Subscribe. Change Password on Login. Viewed 6240 times since Tue, Feb 16, 2016. 2016 Change Password on Login Users may be prompted to change their password upon logging into the MCCC Portal if their password has either expired or been marked for change. Users will see this screen if this is required: If any difficulty…
Inference — PyMC3 3.11.4 documentation
https://docs.pymc.io/en/v3/api/inference.html
Inference¶ Sampling¶. Functions for MCMC sampling. pymc3.sampling. fast_sample_posterior_predictive (trace: MultiTrace | Dataset | InferenceData | list [dict [str, np.ndarray]], samples: int | None = None, model: Model | None = None, var_names: list [str] | None = None, keep_size: bool = False, random_seed = None) → dict [str, np.ndarray] ¶ Generate posterior predictive samples from a model given a trace. This is a vectorized alternative to the standard sample_posterior_predictive …
PyMC3 Documentation — PyMC3 3.11.4 documentation
PyMC3 provides rich support for defining and using GPs. Variational inference saves computational cost by turning a problem of integration into one of optimization. PyMC3’s variational API supports a number of cutting edge algorithms, as well as minibatch for scaling to large datasets. Theano is the deep-learning library PyMC3 uses to construct …
pymc3 – PyPI
https://pypi.org/project/pymc3
PyMC3 is a Python package for Bayesian statistical modeling and Probabilistic Machine Learning focusing on advanced Markov chain Monte Carlo (MCMC) and variational inference (VI) algorithms. Its flexibility and extensibility make it applicable to a large suite of problems.
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