16 citations · 57 across the 25 of their papers we have counts for
6 papers · 1 filter
Modelling Correlated Bernoulli Data Part II: Inference
Louise Kimpton, Peter Challenor, Henry Wynn
Binary data are highly common in many applications, however it is usually modelled with the assumption that the data are independently and identically distributed. This is typicall…
Active Discrimination Learning for Gaussian Process Models
Elham Yousefi, Luc Pronzato, Markus Hainy +2
The paper covers the design and analysis of experiments to discriminate between two Gaussian process models, such as those widely used in computer experiments, kriging, sensor loca…
Binary De Bruijn Processes
Louise Kimpton, Peter Challenor, Henry Wynn
Binary time series data are very common in many applications, and are typically modelled independently via a Bernoulli process with a single probability of success. However, the pr…
Experimental Design Issues in Big Data. The Question of Bias
Elena Pesce, Eva Riccomagno, Henry P. Wynn
Data can be collected in scientific studies via a controlled experiment or passive observation. Big data is often collected in a passive way, e.g. from social media. In studies of…
The algebraic method in experimental design
Hugo Maruri-Aguilar, Henry P. Wynn
The algebraic method provides useful techniques to identify models in designs and to understand aliasing of polynomial models. The present note surveys the topic of Gröbner bases i…
Minimal average degree aberration and the state polytope for experimental designs
Yael Berstein, Hugo Maruri-Aguilar, Shmuel Onn +2
For a particular experimental design, there is interest in finding which polynomial models can be identified in the usual regression set up. The algebraic methods based on Groebner…