Release Notes#


released on 2022-06-14

  • fixed multiple bugs when using the distributions added in v. 3.3.0 with the connect_via_regression function.

  • fixed import bug for print_child_tree and print_operand_tree.

  • fixed a scoping error when supplying a variable as kernel to the construct_gaussian_process function.


released on 2022-05-31

  • new distributions (not all new distributions support all inference methods)
    • Catergorical distribution (Multinoulli)

    • Poisson distribution

    • Exponential distribution

    • Laplace distribution

  • models now accept initial values


released on 2021-08-12

  • fixed a bug that caused long model build times for deep graphs

  • technical adaptions of show() function for the new halerium platform


released on 2021-07-01

  • Time Series modeling
    • introducing the TimeShift operator and the TimeIndex, which allow the user to breach the conditional independence along the data axis to build time series graphs, where variables can depend on their own past.

  • Gaussian processes
  • Metric Gaussian Variational Inference


released on 2021-05-07

  • Causal calculus
    • introducing the do_operation(), which transforms a Graph or other scopetor by applying the do operation to one or more of its variables.

    • introducing the InterventionPredictor objective which calculates predictions for mixes of observations and interventions.

  • added features in the CausalStructure class
  • Gaussian process regression (gaussian_process_regression())
    • better construction of the posterior graph for Gaussian process regression

  • Bugfixes:
    • specification json files created in python 3.8 could not be loaded in python 3.7 due to the upgrade of the pickle version. The pickle version is now set to 4 to ensure compatibility.

    • fixed a bug that cause false alerts in cyclic dependency checks.

    • fixed a bug, where the ADVI model failed for Dirac distributed Variables.

    • fixed a bug in the conjugate gradient implementation that affected the performance of the MGVIModel and the MAPFisher model