One of the core problems of modern statistics is to approximate difficult-to-compute
probability densities. This problem is especially important in Bayesian statistics, which …

Probabilistic modeling is iterative. A scientist posits a simple model, fits it to her data, refines
it according to her analysis, and repeats. However, fitting complex models to large data is a …

Statistical models of text have become increasingly popular in statistics and computer
science as a method of exploring large document collections. Social scientists often want to …

Variational inference is a scalable technique for approximate Bayesian inference. Deriving
variational inference algorithms requires tedious model-specific calculations; this makes it …

Graph neural networks (GNNs) have been extensively used in a wide variety of domains in
recent years. Owing to their power in analyzing graph-structured data, they have become …

Nowadays, many projects and product managers, industry, and portfolio leads understand
that data from the project or portfolio can be valuable for increasing their activities. There are …

Forecasting traffic accidents at a fine-grained spatial scale is essential to provide effective
precautions and improve traffic safety in smart urban sensing applications. Current solutions …

There is a lack of multi-session P300 datasets for Brain-Computer Interfaces (BCI). Publicly
available datasets are usually limited by small number of participants with few BCI sessions …

Bayesian nonlinear system identification for one of the major classes of dynamic model, the
nonlinear autoregressive with exogenous input (NARX) model, has not been widely studied …

Recently, auto-encoder-based generative models have been widely used successfully for
image processing. However, there are few studies on the realization of continuous input …