What does PyMC stand for?
PyMC (formerly known as PyMC3) is a Python package for Bayesian statistical modeling and probabilistic machine learning which focuses on advanced Markov chain Monte Carlo and variational fitting algorithms. It is a rewrite from scratch of the previous version of the PyMC software.
How PyMC works?
PyMC3 relies on Theano to analytically compute model gradients via automatic differentiation of the posterior density. NUTS also has several self-tuning strategies for adaptively setting the tunable parameters of Hamiltonian Monte Carlo.
What is PyMC3 used for?
PyMC3 is a probabilistic programming package for Python that allows users to fit Bayesian models using a variety of numerical methods, most notably Markov chain Monte Carlo (MCMC) and variational inference (VI). Its flexibility and extensibility make it applicable to a large suite of problems.
How do you cite PyMC3?
Citing PyMC3 Salvatier J., Wiecki T.V., Fonnesbeck C. (2016) Probabilistic programming in Python using PyMC3. PeerJ Computer Science 2:e55 DOI: 10.7717/peerj-cs.
What is NO U TURN sampler?
We introduce the No-U-Turn Sampler (NUTS), an extension to HMC that eliminates the need to set a number of steps L. NUTS uses a recursive algorithm to build a set of likely candidate points that spans a wide swath of the target distribution, stopping automatically when it starts to double back and retrace its steps.
What is Pgmpy?
pgmpy [pgmpy] is a python library for working with graphical models. It al- lows the user to create their own graphical models and answer inference or map queries over them. pgmpy has implementation of many inference algorithms like VariableElimination, Belief Propagation etc.
How does MCMC sampling work?
Markov Chain Monte Carlo (MCMC) methods are a class of algorithms for sampling from a probability distribution based on constructing a Markov chain that has the desired distribution as its stationary distribution. The state of the chain after a number of steps is then used as a sample of the desired distribution.
What is Libpgm?
libpgm is an endeavor to make Bayesian probability graphs easy to use. The effort originates from Daphne Koller and Nir Friedman’s Probabilistic Graphical Models (2009), which provides an in-depth study of probabilistic graphical models and their applications.
Why is there a Bayesian network?
Bayesian networks are a type of Probabilistic Graphical Model that can be used to build models from data and/or expert opinion. They can be used for a wide range of tasks including prediction, anomaly detection, diagnostics, automated insight, reasoning, time series prediction and decision making under uncertainty.
Why is Gibbs sampling useful?
Gibbs Sampling is applicable when the joint distribution is not known explicitly or is difficult to sample from directly, but the conditional distribution of each variable is known and is easier to sample from.
What are some advantages of Gibbs sampling?
The advantage of Gibbs sampling are as follows: (1) it is easy to evaluate the conditional distributions, (2) conditionals may be conjugate and we can sample from them exactly, (3) conditionals will be lower dimensional and we can apply rejection sampling or importance sampling.
Why is MCMC necessary?
The goal of MCMC is to draw samples from some probability distribution without having to know its exact height at any point(We don’t need to know C). If the “wandering around” process is set up correctly, you can make sure that this proportionality (between time spent and the height of the distribution) is achieved.