Together with the rest of the Greek Stochastics team we are organising the Greek Stochastics θ' Workshop, at Tinos, Greece, 10-13 July 2016. The theme this year will be on Big Data and Models. Here is the link for more details.
Christmas Workshop on Sequential Monte Carlo and related methods, Monday 21 December - Wednesday 23 December 2015. The details can be found here.
My research focuses on developing Monte Carlo methodology for inference and optimisation problems with emphasis to particle methods. I am also interested in numerical methods for a variety of problems such as:
- Non-linear filtering
- Stochastic optimal control
- Parameter inference for general state space models
- Data assimilation and inverse problems
- Rare events estimation
Some relevant past applications include:
- inverse problems for the Navier-Stokes equation
- electrical power scheduling from renewable sources
- air traffic management: conflict detection and resolution for avionics
- sensor management for target tracking and trajectory planning problems
- distributed inference for sensor networks
- risk sensitive portfolio optimisation
- Calculating principal eigen-functions of non-negative integral kernels: particle approximations and applications,
N. Whiteley and N. Kantas,
Mathematics of Operations Research, to appear.
- On the Convergence of Adaptive Sequential Monte Carlo Methods,
A. Beskos, A. Jasra, N. Kantas and A. Thiery,
Annals of Applied Probability, to appear.
- On Particle Methods for Parameter Estimation in General
N. Kantas, A. Doucet, S. S. Singh, J. M. Maciejowski and N. Chopin,
Statistical Science, Vol. 30, No. 3, 328-351, 2015.
Code used for paper available here
- Sequential Monte Carlo Methods for High-Dimensional Inverse Problems: A case study for the Navier-Stokes equations,
N. Kantas, A. Beskos and A. Jasra,
SIAM/ASA Journal of Uncertainty Quantification, 2, 464-489, 2014. [arXiv]
- Approximate inference for observation driven time series models,
A. Jasra, N. Kantas and E. Ehrlich,
ACM Transactions of Modeling and Computer Simulation (TOMACS), Vol. 24, No. 3, Article 13, 2014. [arXiv]
- Gradient Free Parameter Estimation for Hidden Markov Models with Intractable Likelihoods,
E. Ehrlich, A. Jasra and N. Kantas,
Methodology and Computing in Applied Probability, to appear, 2014.
- Bayesian Parameter Inference for Partially Observed Stopped Processes,
A. Jasra, N. Kantas and A. Persing,
Statistics and Computing, vol 24, Issue 1, pp 1-20, 2014. [arXiv]
- 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]
- 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],
Code used for paper available here
Based Bayesian Optimal Design of Aircraft Trajectories for Air Traffic
N. Kantas, A. Lecchini-Visintini, J. M.
International Journal of Adaptive Control and Signal Processing, vol
24, Issue 10, pp. 882-899, 2010.
Optimal Sensor Scheduling with Application to Observer Trajectory
S.S. Singh, N. Kantas, B. Vo, A. Doucet and R. Evans,
43, no. 5, pp. 817-830, 2007.
Carlo for Model Predictive Control,
N. Kantas, J. M. Maciejowski, A.
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.
- Stable Markov decision processes using simulation based
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)
Sequential Monte Carlo methods for parameter estimation on
general state space models,
N. Kantas, A. Doucet, S.S. Singh, J. M.
In Proc. 15th
IFAC Symposium on System Identification (SYSID) 2009, Saint-Malo,
France, (invited paper).
Model Predictive Control
using Markov Chain Monte Carlo Optimisation,
E. Siva, P.
J.M. Maciejowski, N. Kantas,
In Proc. 10th European Control Conference
(ECC) 2009, Budapest, Hungary.
Self-Localization and Tracking in Sensor Networks,
Kantas, S. S. Singh, A. Doucet,
In Proc of the International Symposium on Image and Signal Processing
and Analysis (ISPA) 2007, Istanbul,
using Particle Methods,
S. S. Singh, A. Doucet,
In Proc. of the Nonlinear Statistical Signal Processing Workshop
(NSSPW) 2006, Cambridge, UK.
Distributed Recursive Maximum Likelihood Implementation for Sensor
N. Kantas, S. S. Singh,
In Proc. of the 9th International Conference on Information Fusion
(Fusion) 2006, Florence, Italy.
with Application to Observer Trajectory Planning,
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,
Technical Reports and Supplementary Material
Statistics and Applied Probability