| Accelerating Metropolis-Hastings algorithms by delayed acceptance M Banterle, C Grazian, A Lee, CP Robert arXiv preprint arXiv:1503.00996, 2015 | 30 | 2015 |
| Application of machine learning techniques to tuberculosis drug resistance analysis DACCC Samaneh Kouchaki, Yang Yang, T Walker, A Sarah Walker, Daniel J Wilson ... Bioinformatics 35 (13), 2276-2282, 2019 | 19 | 2019 |
| Validating a 14-drug microtiter plate containing bedaquiline and delamanid for large-scale research susceptibility testing of Mycobacterium tuberculosis PMV Rancoita, F Cugnata, ALG Cruz, E Borroni, SJ Hoosdally, TM Walker, ... Antimicrobial agents and chemotherapy 62 (9), 2018 | 19 | 2018 |
| Approximating the Likelihood in ABC CC Drovandi, C Grazian, K Mengersen, C Robert Handbook of approximate bayesian computation, 321-368, 2018 | 15 | 2018 |
| Jeffreys’ priors for mixture estimation C Grazian, CP Robert Bayesian statistics from methods to models and applications, 37-48, 2015 | 12 | 2015 |
| Approximate Bayesian inference in semiparametric copula models C Grazian, B Liseo arXiv preprint arXiv:1503.02912, 2015 | 11 | 2015 |
| Accelerating Metropolis-Hastings algorithms: Delayed acceptance with prefetching M Banterle, C Grazian, CP Robert arXiv preprint arXiv:1406.2660, 2014 | 10 | 2014 |
| Accelerating Metropolis-Hastings algorithms by delayed acceptance M Banterle, C Grazian, A Lee, CP Robert Foundations of Data Science 1 (2), 103, 2019 | 9 | 2019 |
| Approximate integrated likelihood via ABC methods C Grazian, B Liseo Statistics and its interface 8 (2), 161-171, 2015 | 9 | 2015 |
| DeepAMR for predicting co-occurrent resistance of Mycobacterium tuberculosis DAC Yang Yang, Timothy M Walker, A Sarah Walker, Daniel J Wilson, Timothy E ... Bioinformatics, 1-10, 2019 | 7* | 2019 |
| Jeffreys priors for mixture estimation: Properties and alternatives C Grazian, CP Robert Computational Statistics & Data Analysis 121, 149-163, 2018 | 7 | 2018 |
| Approximate Bayesian computation for copula estimation C Grazian, B Liseo Statistica 75 (1), 111-127, 2015 | 6 | 2015 |
| GenomegaMap: within-species genome-wide d_N/d_S estimation from over 10,000 genomes Molecular Biology and Evolution, 2020 | 5 | 2020 |
| GenomegaMap: within-species genome-wide d_N/d_S estimation from over 10,000 genomes Molecular Biology and Evolution, 2020 | 5* | 2020 |
| Automated detection of bacterial growth on 96-well plates for high-throughput drug susceptibility testing of Mycobacterium tuberculosis PW Fowler, ALG Cruz, SJ Hoosdally, L Jarrett, E Borroni, ... Microbiology 165 (5), 585-585, 2018 | 4 | 2018 |
| On a loss-based prior for the number of components in mixture models C Grazian, C Villa, B Liseo Statistics & Probability Letters 158, 108656, 2020 | 3 | 2020 |
| A review of approximate Bayesian computation methods via density estimation: Inference for simulator‐models C Grazian, Y Fan Wiley Interdisciplinary Reviews: Computational Statistics 12 (4), e1486, 2020 | 2 | 2020 |
| Reconstruction of dispersal patterns of hypervirulent meningococcal strains of serogroup C: cc11 by phylogenomic time trees AL Presti, A Neri, C Fazio, P Vacca, L Ambrosio, C Grazian, B Liseo, ... Journal of clinical microbiology 58 (1), 2019 | 2 | 2019 |
| Deep fundamental factor models MF Dixon, NG Polson arXiv preprint arXiv:1903.07677, 2019 | 2 | 2019 |
| New formulation of the logistic-gaussian process to analyze trajectory tracking data G Mastrantonio, C Grazian, S Mancinelli, E Bibbona arXiv preprint arXiv:1808.00436, 2018 | 2 | 2018 |
