LostTech.TensorFlow.Probability (nuget): TensorFlow Probability is a library for probabilistic reasoning and statistical analysis in TensorFlow. As part of the TensorFlow ecosystem, TensorFlow Probability provides integration of probabilistic methods with deep networks, gradient-based inference via automatic differentiation, and scalability to large datasets and models via hardware acceleration (e.g., GPUs) and distributed computation.  TensorFlow Probability is built on top of TensorFlow.  Basic building blocks are: - Distributions: A large collection of probability distributions and related statistics with batch and broadcasting semantics. - Bijectors: Reversible and composable transformations of random variables. Bijectors provide a rich class of transformed distributions, from classical examples like the log-normal distribution to sophisticated deep learning models such as masked autoregressive flows.  For higher-level module building: - Joint Distributions (e.g., distributions.JointDistributionSequential): Joint distributions over one or more possibly-interdependent distributions. - Probabilistic Layers (layers namespace): Neural network layers with uncertainty over the functions they represent, extending TensorFlow layers  Probabilistic Inference: - Markov chain Monte Carlo (mcmc namespace): Algorithms for approximating integrals via sampling. Includes Hamiltonian Monte Carlo, random-walk Metropolis-Hastings, and the ability to build custom transition kernels. - Variational Inference (vi namespace): Algorithms for approximating integrals via optimization. - Optimizers (optimizer namespace): Stochastic optimization methods, extending TensorFlow optimizers. Includes Stochastic Gradient Langevin Dynamics. - Monte Carlo (monte_carlo namespace): Tools for computing Monte Carlo expectations.  TensorFlow Probability is under active development. Interfaces may change at any time.
Keywords: tensorflow|probability|statistics|bayesian|machine-learning|ML|deep-learning