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@fcooper8472 fcooper8472 released this 18 Aug 15:55
v0.3.0
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Learn about vigilant mode.

This is the first release in a while for PINTS, and while it's still not a 1.0.0 version, the code has been quite stable, and growth has mainly been through the addition of new features! These include:

  • New MCMC sampling methods, including slice sampling methods, relativistic and monomial gamma HMC, and an overhaul of the "adaptive covariance MCMC" method, which now supports several variants.
  • New optimisers (added for insight, not performance), including Nelder-Mead, a gradient descent method and a bare-bones implementation of CMA-ES
  • Several new error measures, log likelihoods, and log priors.
  • New toy problems, such as the eight-schools problem, the german credit problem, stochastic degradation, and the simple harmonic oscillator
  • Noise generating methods, which can be combined with the new autoregressive likelihoods to investigate the consequences of choosing different noise models
  • New diagnostic plots
  • Support for arbitrarily shaped boundaries
  • An MCMCSummary object, and a fix to the rhat method
  • Various bugfixes, improvements to documentation, and new example notebooks

Finally, PINTS is now on PyPI, so that you'll be able to install it with pip install pints without first downloading the repository

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