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J. Griffin

17 papers hereh-index 303.7k citations91 works total

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

author position
  • sole author1
  • first author5
  • middle author6
  • last author5

Across the 17 of 17 papers where every author was matched, so the position is known.

fields
  • stat.ME9
  • stat.CO5
  • cs.CR1
  • q-bio.GN1
  • stat.AP1
same name
  • J. Griffin — 5 papers, h 6
  • J. Griffin — 5 papers, h 4
  • J. Griffin — 4 papers, h 20
  • J. Griffin — 4 papers, h 12
  • J. Griffin — 4 papers, h 38
  • J. Griffin — 2 papers, h 3

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
20092023
most citedTwo-sample Bayesian Nonparametric Hypothesis Testing

54 citations · 91 across the 16 of their papers we have counts for

collaborators
Showing 2022Show all

4 papers · 1 filter

stat.AP2022★ 5 cited

eDNAPlus: A unifying modelling framework for DNA-based biodiversity monitoring

Alex Diana, Eleni Matechou, Jim Griffin +5

DNA-based biodiversity surveys involve collecting physical samples from survey sites and assaying the contents in the laboratory to detect species via their diagnostic DNA sequence…

stat.ME2022

Bayesian inference of vector autoregressions with tensor decompositions

Yiyong Luo, Jim E. Griffin

Vector autoregressions (VARs) are popular model for analyzing multivariate economic time series. However, VARs can be over-parameterized if the numbers of variables and lags are mo…

stat.ME2022

Normalized Latent Measure Factor Models

Mario Beraha, Jim E. Griffin

We propose a methodology for modeling and comparing probability distributions within a Bayesian nonparametric framework. Building on dependent normalized random measures, we consid…

stat.ME2022★ 6 cited

A loss discounting framework for model averaging and selection in time series models

Dawid Bernaciak, Jim E. Griffin

We introduce a Loss Discounting Framework for model and forecast combination which generalises and combines Bayesian model synthesis and generalized Bayes methodologies. We use a l…

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