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- Arnaud Doucet

Mathematics

UK

2023

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

Engineering and Technology
D-index
70
Citations
56,866
261
World Ranking
283
National Ranking
19

Mathematics
D-index
77
Citations
61,601
325
World Ranking
105
National Ranking
6

2023 - Research.com Mathematics in United Kingdom Leader Award

- Statistics
- Artificial intelligence
- Mathematical analysis

His primary areas of investigation include Particle filter, Algorithm, Monte Carlo method, Markov chain Monte Carlo and Mathematical optimization. His Particle filter research includes elements of Estimation theory, State space, Auxiliary particle filter and Importance sampling. Arnaud Doucet focuses mostly in the field of Algorithm, narrowing it down to matters related to Smoothing and, in some cases, Speech recognition and Maximum a posteriori estimation.

In his work, Quantum Monte Carlo is strongly intertwined with Hidden Markov model, which is a subfield of Monte Carlo method. His Markov chain Monte Carlo research focuses on Posterior probability and how it relates to Inference. His study in Mathematical optimization is interdisciplinary in nature, drawing from both Filter, Optimal estimation, Filtering problem and Markov chain, Markov model.

- Sequential Monte Carlo methods in practice (5985 citations)
- On sequential Monte Carlo sampling methods for Bayesian filtering (3903 citations)
- An introduction to MCMC for machine learning (1784 citations)

His primary areas of study are Particle filter, Markov chain Monte Carlo, Algorithm, Monte Carlo method and Mathematical optimization. Particle filter is the subject of his research, which falls under Artificial intelligence. His Markov chain Monte Carlo research is multidisciplinary, incorporating perspectives in Statistical physics, Markov chain, Applied mathematics and Bayesian inference.

Arnaud Doucet combines subjects such as Sampling, Posterior probability, State space and Gibbs sampling with his study of Algorithm. His biological study focuses on Monte Carlo integration. His research integrates issues of Stochastic approximation, Filter, Markov process, Expectation–maximization algorithm and Hidden Markov model in his study of Mathematical optimization.

- Particle filter (42.46%)
- Markov chain Monte Carlo (38.62%)
- Algorithm (36.57%)

- Applied mathematics (19.95%)
- Markov chain Monte Carlo (38.62%)
- Particle filter (42.46%)

Applied mathematics, Markov chain Monte Carlo, Particle filter, Algorithm and Estimator are his primary areas of study. Arnaud Doucet studies Markov chain Monte Carlo, focusing on Hybrid Monte Carlo in particular. His studies examine the connections between Hybrid Monte Carlo and genetics, as well as such issues in Monte Carlo molecular modeling, with regards to Quantum Monte Carlo, Markov chain mixing time and Monte Carlo method in statistical physics.

His research in Particle filter intersects with topics in Resampling, Monte Carlo method, Mathematical optimization and Reinforcement learning. His work on Monte Carlo method is being expanded to include thematically relevant topics such as Probability distribution. His Algorithm study integrates concerns from other disciplines, such as Dimension, Kernel, Inference, Sampling and Invariant.

- On Markov chain Monte Carlo methods for tall data (115 citations)
- The Bouncy Particle Sampler: A Nonreversible Rejection-Free Markov Chain Monte Carlo Method (104 citations)
- Augmented Neural ODEs (97 citations)

- Statistics
- Artificial intelligence
- Mathematical analysis

Arnaud Doucet mostly deals with Markov chain Monte Carlo, Applied mathematics, Particle filter, Markov process and Monte Carlo method. In the field of Markov chain Monte Carlo, his study on Hybrid Monte Carlo overlaps with subjects such as Context. His work in Hybrid Monte Carlo addresses issues such as Monte Carlo molecular modeling, which are connected to fields such as Mathematical optimization, Markov chain mixing time and Monte Carlo method in statistical physics.

Particle filter and Uncertainty quantification are commonly linked in his work. The various areas that Arnaud Doucet examines in his Markov process study include Statistical physics, Parallel tempering and Piecewise. His studies deal with areas such as Econometrics, Algorithm, Estimation theory and Bayesian inversion as well as Monte Carlo method.

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.

Sequential Monte Carlo methods in practice

Arnaud Doucet;Nando De Freitas;Neil Gordon;Adrian Smith.

**(2001)**

10036 Citations

Sequential Monte Carlo methods in practice

Arnaud Doucet;Nando De Freitas;Neil Gordon;Adrian Smith.

**(2001)**

10036 Citations

An Introduction to Sequential Monte Carlo Methods

Arnaud Doucet;Nando de Freitas;Neil J. Gordon.

Sequential Monte Carlo Methods in Practice **(2001)**

8433 Citations

An Introduction to Sequential Monte Carlo Methods

Arnaud Doucet;Nando de Freitas;Neil J. Gordon.

Sequential Monte Carlo Methods in Practice **(2001)**

8433 Citations

On sequential Monte Carlo sampling methods for Bayesian filtering

Arnaud Doucet;Simon Godsill;Christophe Andrieu.

Statistics and Computing **(2000)**

6197 Citations

On sequential Monte Carlo sampling methods for Bayesian filtering

Arnaud Doucet;Simon Godsill;Christophe Andrieu.

Statistics and Computing **(2000)**

6197 Citations

An introduction to MCMC for machine learning

Christophe Andrieu;Nando De Freitas;Arnaud Doucet;Michael I. Jordan.

Machine Learning **(2003)**

3127 Citations

An introduction to MCMC for machine learning

Christophe Andrieu;Nando De Freitas;Arnaud Doucet;Michael I. Jordan.

Machine Learning **(2003)**

3127 Citations

The Unscented Particle Filter

Rudolph van der Merwe;Arnaud Doucet;Nando de Freitas;Eric A. Wan.

neural information processing systems **(2000)**

2489 Citations

The Unscented Particle Filter

Rudolph van der Merwe;Arnaud Doucet;Nando de Freitas;Eric A. Wan.

neural information processing systems **(2000)**

2489 Citations

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