5 papers
A taxonomy of estimator consistency on discrete estimation problems
Michael Brand, Thomas Hendrey
We describe a four-level hierarchy mapping both all discrete estimation problems and all estimators on these problems, such that the hierarchy describes each estimator's consistenc…
Risk-averse estimation, an axiomatic approach to inference, and Wallace-Freeman without MML
Michael Brand
We define a new class of Bayesian point estimators, which we refer to as risk averse. Using this definition, we formulate axioms that provide natural requirements for inference, e.…
In-database connected component analysis
Harald Bögeholz, Michael Brand, Radu-Alexandru Todor
We describe a Big Data-practical, SQL-implementable algorithm for efficiently determining connected components for graph data stored in a Massively Parallel Processing (MPP) relati…
RKL: a general, invariant Bayes solution for Neyman-Scott
Michael Brand
Neyman-Scott is a classic example of an estimation problem with a partially-consistent posterior, for which standard estimation methods tend to produce inconsistent results. Past a…
Lower bounds on the Münchhausen problem
Michael Brand
"The Baron's omni-sequence", B(n), first defined by Khovanova and Lewis (2011), is a sequence that gives for each n the minimum number of weighings on balance scales that can verif…