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researcher

G. Martin

30 papers hereh-index 201.7k citations96 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author7
  • middle author16
  • last author5

Across the 28 of 30 papers where every author was matched, so the position is known.

fields
  • stat.ME14
  • stat.CO5
  • econ.EM3
  • q-fin.ST3
  • stat.AP3
  • math.ST2
same name
  • G. Martin — 62 papers, h 17
  • G. Martin — 40 papers, h 31
  • G. Martin — 30 papers, h 18
  • G. Martin — 25 papers, h 22
  • G. Martin — 21 papers, h 15
  • G. Martin — 18 papers, h 9

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20132024
most citedForecasting: theory and practice

27 citations · 74 across the 13 of their papers we have counts for

collaborators
Showing 2021Show all

4 papers · 1 filter

stat.ME2021★ 18 cited

Approximating Bayes in the 21st Century

Gael M. Martin, David T. Frazier, Christian P. Robert

The 21st century has seen an enormous growth in the development and use of approximate Bayesian methods. Such methods produce computational solutions to certain intractable statist…

stat.AP2021

ABC of the Future

Henri Pesonen, Umberto Simola, Alvaro Köhn-Luque +8

Approximate Bayesian computation (ABC) has advanced in two decades from a seminal idea to a practically applicable inference tool for simulator-based statistical models, which are…

stat.ME2021

Variational Bayes in State Space Models: Inferential and Predictive Accuracy

David T. Frazier, Ruben Loaiza-Maya, Gael M. Martin

Using theoretical and numerical results, we document the accuracy of commonly applied variational Bayes methods across a range of state space models. The results demonstrate that,…

stat.ME2021

Loss-Based Variational Bayes Prediction

David T. Frazier, Ruben Loaiza-Maya, Gael M. Martin +1

We propose a new approach to Bayesian prediction that caters for models with a large number of parameters and is robust to model misspecification. Given a class of high-dimensional…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.