Abstract: We develop algorithms for sampling from a probability distribution on a sub-manifold embedded in Rn. Applications are given to the evaluation of algorithms in ‘Topo-logical Statistics’; to goodness of fit tests in exponential families and to Neyman’s smooth test. This article is partially expository, giving an introduction to the tools of geometric measure theory
In this article we explore an algorithm for diffeomorphic random sampling of nonuniform probability ...
Metropolis Hastings nested sampling evolves a Markov chain, accepting new points along the chain acc...
We survey recent progress and many open questions in the field of sampling high-dimensional distribu...
Abstract: We develop algorithms for sampling from a probability distribution on a submanifold embedd...
International audienceWith the possibility of interpreting data using increasingly complex models, w...
International audienceWe describe a new methodology for constructing probability measures from obser...
A systematic introduction to a general nonparametric theory of statistics on manifolds, with emphasi...
International audienceWe present a new approach for sampling probability models supported on a manif...
We propose a theoretically justified and practically applicable slice sampling based Markov chain Mo...
The hypothesis that high dimensional data tends to lie in the vicinity of a low di-mensional manifol...
AbstractIn a previous investigation we studied some asymptotic properties of the sample mean locatio...
Geometry plays an important role in modern statistical learning theory, and many different aspects o...
This thesis presents certain recent methodologies and some new results for the statistical analysis ...
We want to sketch the support of a probabil-ity measure on Euclidean space from samples that have be...
Using the tools of category theory and differential geometry, we extend the geometric notions conseq...
In this article we explore an algorithm for diffeomorphic random sampling of nonuniform probability ...
Metropolis Hastings nested sampling evolves a Markov chain, accepting new points along the chain acc...
We survey recent progress and many open questions in the field of sampling high-dimensional distribu...
Abstract: We develop algorithms for sampling from a probability distribution on a submanifold embedd...
International audienceWith the possibility of interpreting data using increasingly complex models, w...
International audienceWe describe a new methodology for constructing probability measures from obser...
A systematic introduction to a general nonparametric theory of statistics on manifolds, with emphasi...
International audienceWe present a new approach for sampling probability models supported on a manif...
We propose a theoretically justified and practically applicable slice sampling based Markov chain Mo...
The hypothesis that high dimensional data tends to lie in the vicinity of a low di-mensional manifol...
AbstractIn a previous investigation we studied some asymptotic properties of the sample mean locatio...
Geometry plays an important role in modern statistical learning theory, and many different aspects o...
This thesis presents certain recent methodologies and some new results for the statistical analysis ...
We want to sketch the support of a probabil-ity measure on Euclidean space from samples that have be...
Using the tools of category theory and differential geometry, we extend the geometric notions conseq...
In this article we explore an algorithm for diffeomorphic random sampling of nonuniform probability ...
Metropolis Hastings nested sampling evolves a Markov chain, accepting new points along the chain acc...
We survey recent progress and many open questions in the field of sampling high-dimensional distribu...