Results for "Statistics and Data Science"

Mueller’s areas of expertise include the development and analysis of simulation-based methods for Bayesian inference, including posterior inference and design, and applications in biostatistics and bioinformatics.…

Our laboratory is broadly is interested in problems at the intersection of Computer Science, Genomics and Statistics. In particular we develop scalable and efficient methods to make sense of complex, large-scale…

Formal derivation of algorithms, mechanical generation of libraries, numerical analysis, parallel supercomputing, scientific computing, high-performance linear algebra libraries.

Nonparametric Bayesian methods for use in machine learning applications
Distributions over collections of measures indexed by values in some covariate space