Hands-on example: Bayesian linear regression; prior predictive checks; posterior sampling with NUTS; basic model diagnostics; posterior predictive checks.
talk-data.com
Topic
arviz
5
tagged
Activity Trend
6
peak/qtr
2020-Q1
2026-Q1
Filtering by:
[Online] A Tutorial for Getting Started with PyMC
×
by
Christopher Fonnesbeck
(PyMC Labs; Adjunct Associate Professor, Vanderbilt University Medical Center)
by
Christopher Fonnesbeck
(PyMC Labs; Adjunct Associate Professor, Vanderbilt University Medical Center)
Addressing frequently asked questions; debugging convergence issues; understanding and fixing divergences; performance optimization tips.
by
Christopher Fonnesbeck
(PyMC Labs; Adjunct Associate Professor, Vanderbilt University Medical Center)
Overview of PyMC and its role in the Python data science ecosystem; understanding probabilistic vs Bayesian approaches; a survey of the probabilistic programming landscape; real-world applications and case studies.
by
Christopher Fonnesbeck
(PyMC Labs; Adjunct Associate Professor, Vanderbilt University Medical Center)
Model contexts and random variables; prior and likelihood specification; working with observed data; PyMC's relationship with ArviZ.
by
Christopher Fonnesbeck
(PyMC Labs; Adjunct Associate Professor, Vanderbilt University Medical Center)
ArviZ for visualization and diagnostics; related packages (Bambi, PyMC-experimental); finding and using PyMC example notebooks; community resources and support channels.