Nikolas Kantas

Research Associate,
Dept. of Statistical Science
University College London

Email:
n . kantas AT ucl . ac . uk

Preprints

• Approximate inference for observation driven time series models,
  A. Jasra, N. Kantas and E. Ehrlich, submitted.
• A particle method for approximating principal eigen-functions and related quantities,
  N. Whiteley and N. Kantas, submitted.
• Static parameter estimation for ABC approximations of hidden Markov models,
  E. Ehrlich, A. Jasra and N. Kantas, submitted.
• On Particle Methods for Parameter Estimation in General State-Space Models,
  N. Kantas, A. Doucet, S. S. Singh, J. M. Maciejowski and N. Chopin, submitted. (available upon request)
• Particle methods for Stochastic Regulation: towards an application for power scheduling,
  N. Kantas, P. Del Moral and R. Vinter,
  Imperial College Technical Report. April 2011.(available upon request)

Journal papers

• Linear Variance Bounds for Particle Approximations of Time-Homogeneous Feynman-Kac Formulae,
  N. Whiteley, N. Kantas, A. Jasra,
  Stochastic Processes and their Applications, vol 122, Issue 4, pp. 1840-1865, 2012. [arXiv]
• Bayesian Parameter Inference for Partially Observed Stopped Processes,
  A. Jasra, N. Kantas and A. Persing,
  Statistics and Computing, to appear, 2012. [arXiv]
• Distributed Maximum Likelihood with application to simultaneous self-localization and tracking for sensor networks,
  N. Kantas, S. S. Singh, A. Doucet,
  IEEE Transactions of Signal Processing, vol 60, Issue 10, pp. 5038 - 5047, 2012. [arXiv]
• Simulation Based Bayesian Optimal Design of Aircraft Trajectories for Air Traffic Management,
  N. Kantas, A. Lecchini-Visintini, J. M. Maciejowski,
  International Journal of Adaptive Control and Signal Processing, vol 24, Issue 10, pp. 882-899, 2010.
• Simulation-Based Optimal Sensor Scheduling with Application to Observer Trajectory Planning,
  S. Singh, N. Kantas, B. Vo, A. Doucet and R. Evans,
  Automatica, vol. 43, no. 5, pp. 817-830, 2007.

Book Chapters

• Sequential Monte Carlo for Model Predictive Control,
  N. Kantas, J. M. Maciejowski, A. Lecchini-Visintini,
  In Nonlinear Model Predictive Control Towards New Challenging Applications Series: Lecture Notes in Control and Information Sciences , Vol. 384,Magni, Lalo; Raimondo, Davide Martino; Allgoewer, Frank (Eds.), 2009.

Conference papers

• Stable Markov decision processes using simulation based predictive control,
  Z. Yang, N. Kantas, A. Lecchini-Visintini, J.M. Maciejowski,
  In Proc 19th International Symposium on Mathematical Theory of Networks and Systems, MTNS 2010, 5-9 Jul 2010, Budapest, Hungary, (invited paper)
• Overview of Sequential Monte Carlo methods for parameter estimation on general state space models,
  N. Kantas, A. Doucet, S.S. Singh, J. M. Maciejowski,
  In Proc. 15th IFAC Symposium on System Identification (SYSID) 2009, Saint-Malo, France, (invited paper). 
• Stability of Model Predictive Control using Markov Chain Monte Carlo Optimisation,
  E. Siva, P. Goulart, J.M. Maciejowski, N. Kantas,
  In Proc. 10th European Control Conference (ECC) 2009, Budapest, Hungary.
• Sequential Monte Carlo for Model Predictive Control,
  N. Kantas, J. M. Maciejowski, A. Lecchini-Visintini,
  In Proc. of the International Workshop on Assessment and Future Directions of Nonlinear Model Predictive Control (NMPC) 2008, Pavia, Italy. 
• Distributed Online Self-Localization and Tracking in Sensor Networks,
  N. Kantas, S. S. Singh, A. Doucet,
  In Proc of the International Symposium on Image and Signal Processing and Analysis (ISPA) 2007, Istanbul, Turkey.
• Distributed Self Localisation of Sensor Networks using Particle Methods,
  N. Kantas, S. S. Singh, A. Doucet,
  In Proc. of the Nonlinear Statistical Signal Processing Workshop (NSSPW) 2006, Cambridge, UK.
• A Distributed Recursive Maximum Likelihood Implementation for Sensor Registration,
  N. Kantas, S. S. Singh, A. Doucet,
  In Proc. of the 9th International Conference on Information Fusion (Fusion) 2006, Florence, Italy.
• Simulation-Based Optimal Sensor Scheduling with Application to Observer Trajectory Planning,
  S. S. Singh, N. Kantas, B. Vo, A. Doucet and R. Evans,
  In Proc.of the 44th IEEE Conference on Decision and Control and European Control Conference (CDC-ECC) 2005, Sevilla, Spain.

Technical Reports

• On the convergence of a stochastic optimisation algorithm for optimal observer trajectory planning,
  S. S. Singh, N. Kantas, B. Vo, A. Doucet and R. Evans
  Technical Report CUED/F-INFENG/TR 522, University of Cambridge, Department of Engineering, March 2005.

Ph. D. Thesis

• Sequential Decision Making for General State Space Models, PhD Thesis, Department of Engineering, University of Cambridge, Feb. 2009.