Plate notation is a useful visual method for describing graphical models, but the software can be awkward. Here we demonstrate daft-pgm, a solution using pure Python.
Posts in bayesian-statistics
In the final article of this technical series we demonstrate hierarchical linear regression using PyMC3 to compare vehicle NOx emissions for a range of car manufacturers.
In the second article of this technical series we demonstrate the flexible syntax of PyMC3 with regularized linear modelling of car emissions data and model evaluation.
Bayesian inference bridges the gap between white-box model introspection and black-box predictive performance. This technical series describes some methods using PyMC3, an inferential framework in Python.
We finish our series on Bayesian networks by discussing conditional probability, more complex models, missing data and other real-world issues in their application to insurance modelling.
We continue our series on Bayesian networks by discussing their suitability for fraud detection in complex processes: for example assessing medical non-disclosure in life insurance applications.
Bayesian networks are useful tools for probabilistically computing the interdependencies and outcomes of real-world systems given limited information. Here we describe their use in fraud detection.
In the final article of this series we investigate the behaviours of different distribution distance metrics to let us automatically determine the scale of the change.
In the second article of this series we continue our technical discussion on using Bayesian analysis to probabilistically estimate the effect of a business process change.
In this series of articles we'll discuss the use of probabilistic modelling to help efficiently evaluate changes to business processes and the success of marketing campaigns.