This is a pre-production deployment of Warehouse. Changes made here affect the production instance of PyPI (pypi.python.org).
Help us improve Python packaging - Donate today!

A Python implementation of an Approximate Bayesian Computation Sequential Monte Carlo (ABC SMC) sampler for parameter estimation.

Project Description

Approximate Bayesian computation (ABC) and so called “likelihood free” Markov chain Monte Carlo techniques are popular methods for tackling parameter inference in scenarios where the likelihood is intractable or unknown. These methods are called likelihood free as they are free from the usual assumptions about the form of the likelihood e.g. Gaussian, as ABC aims to simulate samples from the parameter posterior distribution directly. astroABC is a python package that implements an Approximate Bayesian Computation Sequential Monte Carlo (ABC SMC) sampler as a python class. It is extremely flexible and applicable to a large suite of problems. astroABC requires NumPy,“SciPy“ and sklearn. mpi4py and multiprocessing are optional.

Release History

Release History

This version
History Node

1.4.2

History Node

1.3.2

History Node

1.2.2

History Node

1.1.2

History Node

1.0.2

History Node

1.0.1

History Node

1.0.0

Supported By

WebFaction WebFaction Technical Writing Elastic Elastic Search Pingdom Pingdom Monitoring Dyn Dyn DNS Sentry Sentry Error Logging CloudAMQP CloudAMQP RabbitMQ Heroku Heroku PaaS Kabu Creative Kabu Creative UX & Design Fastly Fastly CDN DigiCert DigiCert EV Certificate Rackspace Rackspace Cloud Servers DreamHost DreamHost Log Hosting