inference backend distributions parameters modules models fitting callbacks datagenerators predicting evaluating inspecting applications saving_and_loading math
For a quick start, take a look at the ../examples/examples.
The user guide contains more detailed information about using ProbFlow, including:
- A brief description of
Bayesian modeling <inference>, - Using
distributions,parameters, andmodulesto create Bayesianmodels, - How to
fit those models <fitting>to data, - How to
make predictions <predicting>with those models, - How to
evaluate <evaluating>the performance of a model, - How to
inspect <inspecting>a model's structure and the values of its parameters, - How to use ProbFlow's
applications, - How to perform actions mid-training with
callbacks, - How to load data on-the-fly with
datagenerators, - How to
save and load models <saving_and_loading>, - How to
choose your backend and default datatype <backend>, and mathabout how ProbFlow fits Bayesian models.