TensorProb documentation¶
You’ve found the documentation for TensorProb, a probabilistic programming framework based on TensorFlow.
TensorProb is currently under construction! Expect things to break!
We are working on implementing the following features:
- High flexibility in defining the statistical model
- Models are defined in a self-contained with block
- Seamless switching between frequentist and bayesian paradigms
- Finding the maximum likelihood estimate or MAP estimate using a variety of optimizers
- Flexible sampling using different MCMC backends
- An extensive library of probability distributions
- Analytic and numeric marginalization of probability distributions to support missing data and physical boundaries
- Convolution of probability distributions
- Functions for calculating confidence and credible intervals
- Functions for hypothesis testing
Benefits of using TensorFlow as a backend include
- Fast evaluation of the model using multiple CPU threads and/or GPUs
- Defining new probability distributions using symbolic variables in Python
- Possibility to write new optimized operators in C++ and load them dynamically