Randal Douc
- Artificial Intelligence top 1%
- Statistics and Probability top 0.5%
- Finance top 5%
- Control and Systems Engineering top 5%
- Mathematical Physics top 5%
- Co-authors
- Olivier CappéÉric MoulinesArnaud GuillinJimmy OlssonChristian P. RobertGersende FortPhilippe SoulierDavid S. Stoffer
- Topics
- Bayesian Methods and Mixture Models (17 papers)Markov Chains and Monte Carlo Methods (16 papers)Target Tracking and Data Fusion in Sensor Networks (13 papers)
In The Last Decade
Randal Douc
43 papers receiving 1.6k citations
Hit Papers
Peers
Comparison fields: 5 of 116
- Artificial Intelligence 866
- Statistics and Probability 660
- Finance 233
- Control and Systems Engineering 209
- Mathematical Physics 180
Countries citing papers authored by Randal Douc
This map shows the geographic impact of Randal Douc's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Randal Douc with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Randal Douc more than expected).
Fields of papers citing papers by Randal Douc
This network shows the impact of papers produced by Randal Douc. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Randal Douc. The network helps show where Randal Douc may publish in the future.
Co-authorship network of co-authors of Randal Douc
This figure shows the co-authorship network connecting the top 25 collaborators of Randal Douc. A scholar is included among the top collaborators of Randal Douc based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Randal Douc. Randal Douc is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 84 | |
| 2 | Nonlinear time series: theory, methods and applications with R examples | 44 |
| 3 | Long-term stability of sequential Monte Carlo methods under verifiable conditions | 23 |
| 4 | Partial Ordering of Inhomogeneous Markov Chains with Applications to Markov Chain Monte Carlo Methods | 1 |
| 5 | 47 | |
| 6 | 20 | |
| 7 | 27 | |
| 8 | Consistency of the maximum likelihood estimator for general hidden markov models | 33 |
| 9 | Bounds on Regeneration Times and Limit Theorems for Subgeometric Markov Chains | 8 |
| 10 | 92 | |
| 11 | 20 | |
| 12 | 82 | |
| 13 | 12 | |
| 14 | Adaptive importance sampling in general mixture classes | 116 |
| 15 | 22 | |
| 16 | 24 | |
| 17 | Sequential Monte Carlo smoothing with estimation in non-linear state space models | 2 |
| 18 | Comparison of resampling schemes for particle filteringbreakdown → | 533 |
| 19 | MODERATE DEVIATIONS FOR PARTICLE FILTERING | 6 |
| 20 | 1 |
About Randal Douc
Randal Douc is a scholar working on Statistics and Probability, Finance and Mathematical Physics, having authored 43 papers that have together received 1.7k indexed citations. Recurring topics across this work include Bayesian Methods and Mixture Models (17 papers), Markov Chains and Monte Carlo Methods (16 papers) and Target Tracking and Data Fusion in Sensor Networks (13 papers). The work is most often cited by research in Statistics and Probability (660 citations), Artificial Intelligence (866 citations) and Finance (233 citations). Randal Douc has collaborated with scholars based in France, Sweden and Canada. Frequent co-authors include Olivier Cappé, Éric Moulines, Arnaud Guillin, Jimmy Olsson, Christian P. Robert, Gersende Fort, Philippe Soulier, David S. Stoffer, Catherine Matias and Jeffrey S. Rosenthal. Their work appears in journals such as The Annals of Statistics, Stochastic Processes and their Applications and The Annals of Applied Probability.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.