V 0.7.5

• Improved the appearance of figures produced by plot_gp and added the option
  to draw true parameter indicators on the subplots using the optional input
  true_params
• Modified DCC model by taking into account that subject can't infect herself
• Added ability to set minimizer constrains for BOLFI
• Enable bolfi.fit using only pre-generated initial evidence points
• Fixed a bug causing random seed number to be deterministic
• Updated requirements-dev.txt with pytest>=4.4
• Minor changes to documentation and refactoring
• Added make test-notslow alternative

V0.7.4

• Add sampler option algorithm for bolfi-posterior-sampling
• Add a check whether the option given for algorithm is one if the implemented samplers
• Add metropolis sampler algorithm=metropolis for bolfi-posterior-sampling
• Add option warmup to metropolis-sampler
• Add a small test of metropolis-sampler
• Fix bug in plot_discrepancy for more than 6 parameters
• Implement plot_gp for BayesianOptimization classes for plotting discrepancies
  and pair-wise contours in case when we have arbitrary number of parameters
• Fix lint

Release 0.7.3

• Fix bug in plot_pairs which crashes in case of 1 parameter
• Fix bug in plot_marginals which outputs empty plots in case where we have parameter more than 5
• Fix crashing summary and plots for samples with multivariate priors
• Add progress bar for inference methods
• Add method save to Sample objects
• Add support for giving seed to generate
• Implement elfi.plot_params_vs_node for plotting parameters vs. node output

Release 0.7.2

• Added support for kwargs in elfi.set_client
• Added new example: inference of transmission dynamics of bacteria in daycare centers
• Added new example: Lorenz model

Release 0.7.1

• Implemented model selection (elfi.compare_models). See API documentation.
• Fix threshold=0 in rejection sampling
• Set default batch_size to 1 in ParameterInference base class

Release 0.7

• Added the MaxVar acquisition method
• Added the RandMaxVar acquisition method
• Added the ExpIntVar acquisition method
• Implemented the Two Stage Procedure, a method of summary-statistics diagnostics
• Added new example: the stochastic Lotka-Volterra model
• Fix methods.bo.utils.minimize to be strictly within bounds
• Fix elfi.Distance to support scipy 1.0.0

Release 0.6.3

• Further performance improvements for rerunning inference using stored data via caches
• Added the general Gaussian noise example model (fixed covariance)
• restrict NetworkX to versions < 2.0

Release 0.6.2

Changes

• Easier saving and loading of ElfiModel
• Renamed elfi.set_current_model to elfi.set_default_model
• Renamed elfi.get_current_model to elfi.get_default_model
• Improved performance when rerunning inference using stored data
• Change SMC to use ModelPrior, use to immediately reject invalid proposals

Release 0.6.1

Changes

• Fix elfi.Prior and NoneType error #203
• Fix a bug preventing the reuse of ArrayPool data with a new inference
• Added pickling for OutputPool:s
• Added OutputPool.open to read a closed pool from disk
• Refactored Sample and SmcSample classes
• Added elfi.new_model method
• Made elfi.set_client method to accept clients as strings for easier client switching
• Fixed a bug in NpyArray that would lead to an inconsistent state if multiple simultaneous instances were opened.
• Added the ability to move the pool data folder
• Sample.summary is now a method instead of a property
• SmcSample methods takes the keyword argument 'all' to show results of all populations
• Added a section about iterative advancing to documentation

Release 0.6

• Changed some of the internal variable names in methods.py. Most notable outputs is now
  output_names.
• methods.py renamed to parameter_inference.py
• Changes in elfi.methods.results module class names:
  • OptimizationResult (a new result type)
  • Result -> Sample
  • ResultSMC -> SmcSample
  • ResultBOLFI -> BolfiSample
• Changes in BO/BOLFI:
  • take advantage of priors
  • take advantage of seed
  • improved optimization scheme
  • bounds must be a dict
• two new toy examples added: Gaussian and the Ricker model