Data from: A new method of Bayesian causal inference in non-stationary environments

Bayesian inference is the process of narrowing down the hypotheses
(causes) to the one that best explains the observational data (effects).
To accurately estimate a cause, a considerable amount of data is required
to be observed for as long as possible. However, the object of inference
is not always constant. In this case, a method such as exponential moving
average (EMA) with a discounting rate is used to improve the ability to
respond to a sudden change; it is also necessary to increase the
discounting rate. That is, a trade-off is established in which the
followability is improved by increasing the discounting rate, but the
accuracy is reduced. Here, we propose an extended Bayesian inference
(EBI), wherein human-like causal inference is incorporated. We show that
both the learning and forgetting effects are introduced into Bayesian
inference by incorporating the causal inference. We evaluate the
estimation performance of the EBI through the learning task of a
dynamically changing Gaussian mixture model. In the evaluation, the EBI
performance is compared with those of the EMA and a sequential discounting
expectation-maximization algorithm. The EBI was shown to modify the
trade-off observed in the EMA.