Knowledge of population distribution is critical for building infrastructure, distributing resources, and monitoring the progress of sustainable development goals.
Reliable and frequent population estimation is key for making policies around vaccination and planning infrastructure delivery.
Model criticism is usually carried out by assessing if replicated data generated under the fitted model looks similar to the observed data, see e. g. Gelman, Carlin, Stern, and Rubin [2004, p. 165].
For retrieval of gene expression experiments, we use a probabilistic model called product partition model, which induces a clustering of genes that show similar expression patterns across a number of samples.
To incorporate this information in the retrieval task, we suggest employing a retrieval metric that utilizes probabilistic models learned from the measurements.
The kernel least mean squares (KLMS) algorithm is a computationally efficient nonlinear adaptive filtering method that "kernelizes" the celebrated (linear) least mean squares algorithm.
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.
However, these statistics do not fully describe a point process and thus the tests can be misleading.