| An online expectation–maximization algorithm for changepoint models S Yildirim, SS Singh, A Doucet Journal of Computational and Graphical Statistics 22 (4), 906-926, 2013 | 54 | 2013 |
| Parameter estimation in hidden Markov models with intractable likelihoods using sequential Monte Carlo S Yıldırım, SS Singh, T Dean, A Jasra Journal of Computational and Graphical Statistics 24 (3), 846-865, 2015 | 38 | 2015 |
| Bayesian tracking and parameter learning for non-linear multiple target tracking models L Jiang, SS Singh, S Yıldırım IEEE Transactions on Signal Processing 63 (21), 5733-5745, 2015 | 32 | 2015 |
| Exact MCMC with differentially private moves: revisiting the penalty algorithm in a data privacy framework S Yıldırım, B Ermiş Statistics and Computing 29, 947-963, 2019 | 25 | 2019 |
| On the utility of Metropolis-Hastings with asymmetric acceptance ratio C Andrieu, A Doucet, S Yıldırım, N Chopin arXiv preprint arXiv:1803.09527, 2018 | 24 | 2018 |
| MCMC shape sampling for image segmentation with nonparametric shape priors E Erdil, S Yildirim, M Cetin, T Tasdizen Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2016 | 22 | 2016 |
| A Bayesian deconvolution approach for receiver function analysis S Yildirim, AT Cemgil, M Aktar, Y Ozakin, A Ertuzun IEEE Transactions on Geoscience and Remote Sensing 48 (12), 4151-4163, 2010 | 22 | 2010 |
| Calibrating the Gaussian multi-target tracking model S Yildirim, L Jiang, SS Singh, TA Dean Statistics and Computing 25 (3), 595-608, 2015 | 17* | 2015 |
| Differentially private accelerated optimization algorithms N Kuru, S İlker Birbil, M Gurbuzbalaban, S Yildirim SIAM Journal on Optimization 32 (2), 795-821, 2022 | 16 | 2022 |
| Bayesian Allocation Model: Marginal Likelihood-Based Model Selection for Count Tensors S Yıldırım, MB Kurutmaz, M Barsbey, U Şimşekli, AT Cemgil IEEE Journal of Selected Topics in Signal Processing 15 (3), 560-573, 2020 | 15* | 2020 |
| A real-world application of Markov chain Monte Carlo method for Bayesian trajectory control of a robotic manipulator VT Aghaei, A Ağababaoğlu, S Yıldırım, A Onat ISA transactions 125, 580-590, 2022 | 12 | 2022 |
| A Markov chain Monte Carlo algorithm for Bayesian policy search V Tavakol Aghaei, A Onat, S Yıldırım Systems Science & Control Engineering 6 (1), 438-455, 2018 | 12 | 2018 |
| A hybrid method for deconvolution of Bernoulli-Gaussian processes S Yildirim, AT Cemgil, AB Ertuzun 2009 IEEE International Conference on Acoustics, Speech and Signal …, 2009 | 9 | 2009 |
| Estimation of time-varying AR SαS processes using Gibbs sampling D Gençağa, EE Kuruoğlu, A Ertüzün, S Yıldırım Signal Processing 88 (10), 2564-2572, 2008 | 9 | 2008 |
| Energy optimization of wind turbines via a neural control policy based on reinforcement learning Markov chain Monte Carlo algorithm VT Aghaei, A Ağababaoğlu, B Bawo, P Naseradinmousavi, S Yıldırım, ... Applied Energy 341, 121108, 2023 | 8 | 2023 |
| Supply curves in electricity markets: a framework for dynamic modeling and monte carlo forecasting S Yıldırım, M Khalafi, T Güzel, H Satık, M Yılmaz IEEE Transactions on Power Systems 38 (4), 3056-3069, 2022 | 7 | 2022 |
| Machine learning‐based load distribution and balancing in heterogeneous database management systems A Abdennebi, A Elakaş, F Taşyaran, E Öztürk, K Kaya, S Yıldırım Concurrency and Computation: Practice and Experience 34 (4), e6641, 2022 | 7 | 2022 |
| Scalable Monte Carlo inference for state-space models S Yıldırım, C Andrieu, A Doucet arXiv preprint arXiv:1809.02527, 2018 | 7 | 2018 |
| An online expectation-maximisation algorithm for nonnegative matrix factorisation models S Yildirim, AT Cemgil, SS Singh IFAC Proceedings Volumes 45 (16), 494-499, 2012 | 7 | 2012 |
| Metropolis-Hastings with Averaged Acceptance Ratios C Andrieu, S Yıldırım, A Doucet, N Chopin arXiv preprint arXiv:2101.01253, 2020 | 6 | 2020 |
Sumeetpal Sidhu SinghTibra Foundation Chair in Mathematical Sciences, School of Mathematics, University of Wollongong在 uow.edu.au 的电子邮件经过验证
