One contribution of 17 to a Discussion Meeting Issue ‘Signal processing and inference for the physical sciences’. Subject Areas: pattern recognition, computer modelling and simulation, statistics, mathematical modelling Keywords: probabilistic modelling, Bayesian statistics, non-parametrics, machine learning Author for correspondence
Probability theory forms a natural framework for explaining the impressive success of people at solv...
Increasingly complex applications involve large datasets in combination with nonlinear and high dime...
This chapter provides a overview of Bayesian inference, mostly emphasising that it is a universal me...
Modelling is fundamental to many fields of science and engineering. A model can be thought of as a r...
Discussion on the paper by Dalal, Sid R., Nonparametric Bayes decision theory, part of a round table...
One contribution of 17 to a Discussion Meeting Issue ‘Signal processing and inference for the physic...
One contribution of 17 to a Discussion Meeting Issue ‘Signal processing and inference for the physic...
Modern statistical software and machine learning libraries are enabling semi-automated statistical i...
The Second Bayesian Young Statisticians Meeting (BAYSM 2014) and the research presented here facilit...
Bayesian non-parametric models, despite their theoretical elegance, face a serious computational bur...
Bayesian non-parametric models, despite their theoretical elegance, face a serious computational bur...
The work "Topics in non-parametric Bayesian statistics", by N.L.Hjort, presents recent advances in B...
rights reserved. Readers may make verbatim copies of this document for non-commercial purposes by an...
The work "Topics in non-parametric Bayesian statistics", by N.L.Hjort, presents recent advances in B...
Probability theory forms a natural framework for explaining the impressive success of people at solv...
Probability theory forms a natural framework for explaining the impressive success of people at solv...
Increasingly complex applications involve large datasets in combination with nonlinear and high dime...
This chapter provides a overview of Bayesian inference, mostly emphasising that it is a universal me...
Modelling is fundamental to many fields of science and engineering. A model can be thought of as a r...
Discussion on the paper by Dalal, Sid R., Nonparametric Bayes decision theory, part of a round table...
One contribution of 17 to a Discussion Meeting Issue ‘Signal processing and inference for the physic...
One contribution of 17 to a Discussion Meeting Issue ‘Signal processing and inference for the physic...
Modern statistical software and machine learning libraries are enabling semi-automated statistical i...
The Second Bayesian Young Statisticians Meeting (BAYSM 2014) and the research presented here facilit...
Bayesian non-parametric models, despite their theoretical elegance, face a serious computational bur...
Bayesian non-parametric models, despite their theoretical elegance, face a serious computational bur...
The work "Topics in non-parametric Bayesian statistics", by N.L.Hjort, presents recent advances in B...
rights reserved. Readers may make verbatim copies of this document for non-commercial purposes by an...
The work "Topics in non-parametric Bayesian statistics", by N.L.Hjort, presents recent advances in B...
Probability theory forms a natural framework for explaining the impressive success of people at solv...
Probability theory forms a natural framework for explaining the impressive success of people at solv...
Increasingly complex applications involve large datasets in combination with nonlinear and high dime...
This chapter provides a overview of Bayesian inference, mostly emphasising that it is a universal me...
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