D-Index & Metrics Best Publications

D-Index & Metrics D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines.

Discipline name D-index D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines. Citations Publications World Ranking National Ranking
Mathematics D-index 42 Citations 10,758 179 World Ranking 1195 National Ranking 28

Overview

What is he best known for?

The fields of study he is best known for:

  • Statistics
  • Normal distribution
  • Probability distribution

Anthony N. Pettitt mostly deals with Statistics, Algorithm, Markov chain Monte Carlo, Mathematical optimization and Particle filter. His study looks at the relationship between Statistics and fields such as Econometrics, as well as how they intersect with chemical problems. He works mostly in the field of Markov chain Monte Carlo, limiting it down to concerns involving Posterior probability and, occasionally, Estimation theory.

His Mathematical optimization research includes elements of Bayes factor, Bayesian statistics, Markov model, Variable-order Bayesian network and Hidden Markov model. His work carried out in the field of Markov model brings together such families of science as Marginal likelihood, Bayes' theorem and Macroparasite. Anthony N. Pettitt interconnects Sampling, Monte Carlo method and Model selection in the investigation of issues within Bayesian probability.

His most cited work include:

  • A Non‐Parametric Approach to the Change‐Point Problem (1895 citations)
  • An efficient Markov chain Monte Carlo method for distributions with intractable normalising constants (308 citations)
  • Importance Nested Sampling and the MultiNest Algorithm (307 citations)

What are the main themes of his work throughout his whole career to date?

Anthony N. Pettitt mainly investigates Statistics, Bayesian probability, Mathematical optimization, Algorithm and Markov chain Monte Carlo. As a part of the same scientific study, Anthony N. Pettitt usually deals with the Statistics, concentrating on Econometrics and frequently concerns with Regression analysis. His studies in Bayesian probability integrate themes in fields like Inference and Importance sampling.

His Mathematical optimization study combines topics from a wide range of disciplines, such as Sampling, Bayesian experimental design and Optimal design. His study in the field of Estimation theory is also linked to topics like Particle filter. His research integrates issues of Posterior probability, Applied mathematics and Statistical inference in his study of Markov chain Monte Carlo.

He most often published in these fields:

  • Statistics (32.00%)
  • Bayesian probability (26.00%)
  • Mathematical optimization (22.50%)

What were the highlights of his more recent work (between 2014-2021)?

  • Approximate Bayesian computation (18.00%)
  • Bayesian probability (26.00%)
  • Algorithm (22.50%)

In recent papers he was focusing on the following fields of study:

His main research concerns Approximate Bayesian computation, Bayesian probability, Algorithm, Artificial intelligence and Mathematical optimization. The concepts of his Approximate Bayesian computation study are interwoven with issues in Mathematical model, Parametric statistics, Statistics and Applied mathematics. His studies deal with areas such as Estimation theory, Stochastic modelling, Inference and Data mining as well as Bayesian probability.

He undertakes multidisciplinary investigations into Algorithm and Particle filter in his work. His Mathematical optimization study combines topics in areas such as Posterior probability, Bayesian experimental design and Markov chain Monte Carlo. His Markov chain Monte Carlo research includes themes of Marginal likelihood and Computational biology.

Between 2014 and 2021, his most popular works were:

  • Importance Nested Sampling and the MultiNest Algorithm (188 citations)
  • A Review of Modern Computational Algorithms for Bayesian Optimal Design (111 citations)
  • Bayesian indirect inference using a parametric auxiliary model (77 citations)

In his most recent research, the most cited papers focused on:

  • Statistics
  • Normal distribution
  • Probability distribution

The scientist’s investigation covers issues in Bayesian probability, Algorithm, Mathematical optimization, Approximate Bayesian computation and Artificial intelligence. His research in Bayesian probability intersects with topics in Inference and Random walk. His Algorithm research is multidisciplinary, incorporating elements of Monte Carlo method and Markov chain Monte Carlo.

His Mathematical optimization research incorporates elements of Bayesian experimental design and Bayesian inference. The Approximate Bayesian computation study combines topics in areas such as Estimation theory and Mathematical model. His biological study spans a wide range of topics, including Indirect Inference and Machine learning.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

A Non-Parametric Approach to the Change-Point Problem

A. N. Pettitt.
Journal of The Royal Statistical Society Series C-applied Statistics (1979)

3461 Citations

A Non-Parametric Approach to the Change-Point Problem

A. N. Pettitt.
Journal of The Royal Statistical Society Series C-applied Statistics (1979)

3461 Citations

Importance Nested Sampling and the MultiNest Algorithm

Farhan Feroz;Michael P. Hobson;Ewan Cameron;Anthony N. Pettitt.
The Open Journal of Astrophysics (2019)

627 Citations

Importance Nested Sampling and the MultiNest Algorithm

Farhan Feroz;Michael P. Hobson;Ewan Cameron;Anthony N. Pettitt.
The Open Journal of Astrophysics (2019)

627 Citations

An efficient Markov chain Monte Carlo method for distributions with intractable normalising constants

Jesper Moller;Anthony N. Pettitt;Robert W. Reeves;Kasper K. Berthelsen.
Biometrika (2006)

459 Citations

An efficient Markov chain Monte Carlo method for distributions with intractable normalising constants

Jesper Moller;Anthony N. Pettitt;Robert W. Reeves;Kasper K. Berthelsen.
Biometrika (2006)

459 Citations

Model-based geostatistics. Discussion. Authors' reply

P. J. Diggle;J. A. Tawn;R. A. Moyeed;R. Webster.
Applied statistics (1998)

413 Citations

Marginal likelihood estimation via power posteriors

Nial Friel;Anthony N. Pettitt.
Journal of The Royal Statistical Society Series B-statistical Methodology (2008)

384 Citations

Marginal likelihood estimation via power posteriors

Nial Friel;Anthony N. Pettitt.
Journal of The Royal Statistical Society Series B-statistical Methodology (2008)

384 Citations

Nonparametric Methods in General Linear Models.

A. N. Pettitt;M. L. Puri;P. K. Sen.
Biometrics (1986)

292 Citations

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