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Re-release because of broken wheel file.

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Re-release due to pypi version conflicts.

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Fixes:

  • restore description on pypi
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New features:

  • New transition model: Bivariate random walk

Fixes:

  • various import fixes
  • more stability for complex transformations in the Parser module

Development:

  • moved tests and coverage to Github Actions
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New features:

  • New observation model: Laplace distribution
  • Hyper-parameter optimization now supports "forward-only" algorithm

Fixes:

  • Model evidence of ChangePoint transition model depended on the chosen grid-size
  • RegimeSwitch transition model did not support integer parameter values
  • Jeffreys prior for Gaussian observation model was parametrized on variance, not standard deviation
  • SymPy observation models now support Beta function
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New features:

  • Additional API functions in OnlineStudy
  • Probability Parser for arithmetic operations on inferred (hyper-)parameters
  • Custom likelihood functions (observation models) based on NumPy functions
  • Universal plot method
  • Convenience methods load, set, add, eval

Fixes:

  • Support for besseli function in SymPy models
  • Consistent order of parameters in SymPy/SciPy models
  • Consistent order of parameters in joint-distribution plots
  • Fix to support SymPy 1.1
  • AlphaStableRandomWalk transition model
  • NotEqual transition model

Development:

  • bayesloop now features automatic testing based on TravisCI.
  • Automatic code coverage evaluation by coveralls.io
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Fixes

  • Hotfix for scaling of hyper-prior values in ChangepointStudy, resulting in distorted model evidence values. This bug was introduced in version 1.2.0.
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Fixes

  • Use relative imports only, thereby adding support for Python 3.6
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Algorithm & API changes

  • dill module is a required dependency. Loading and saving Study instances is no longer an optional feature.
  • Major refinements to OnlineStudy. This type of analysis now behaves more like a HyperStudy and continually updates hyper-parameter distributions and transition model probabilities.
  • SymPy priors are not re-normalized. This allows to define priors with a support interval that deviates from the defined parameter grid.
  • get...Distribution()-methods return probability values of (hyper-)parameters, not density values. This allows easier post-processing of (hyper-)parameter distributions.
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Fixes

  • Hotfix for numerically stable computation of average posterior distribution (hyper-study)