  We address the problem of retrieving relevant experiments given a query
experiment, motivated by the public databases of datasets in molecular biology
and other experimental sciences, and the need of scientists to relate to
earlier work on the level of actual measurement data. Since experiments are
inherently noisy and databases ever accumulating, we argue that a retrieval
engine should possess two particular characteristics. First, it should compare
models learnt from the experiments rather than the raw measurements themselves:
this allows incorporating experiment-specific prior knowledge to suppress noise
effects and focus on what is important. Second, it should be updated
sequentially from newly published experiments, without explicitly storing
either the measurements or the models, which is critical for saving storage
space and protecting data privacy: this promotes life long learning. We
formulate the retrieval as a ``supermodelling'' problem, of sequentially
learning a model of the set of posterior distributions, represented as sets of
MCMC samples, and suggest the use of Particle-Learning-based sequential
Dirichlet process mixture (DPM) for this purpose. The relevance measure for
retrieval is derived from the supermodel through the mixture representation. We
demonstrate the performance of the proposed retrieval method on simulated data
and molecular biological experiments.
