Sumeetpal S. Singh

发表

Asymptotic behaviour of the posterior distribution in approximate Bayesian computation

Ajay Jasra, Thomas A. Dean, Sumeetpal S. Singh, 2021, Stochastic Analysis and Applications.

Anytime parallel tempering

Lawrence M. Murray, Alix Marie d'Avigneau, Sumeetpal S. Singh, 2021, Stat. Comput..

Online Particle Smoothing With Application to Map-Matching

Samuel Duffield, Sumeetpal S. Singh, Samuel Duffield, 2020, IEEE Transactions on Signal Processing.

Ensemble Kalman Inversion for General Likelihoods

Samuel Duffield, Sumeetpal S. Singh, 2021 .

Particle approximations of the score and observed information matrix in state space models with application to parameter estimation

Sumeetpal S. Singh, A. Doucet, G. Poyiadjis, 2011 .

A policy gradient method for semi-Markov decision processes with application to call admission control

Arnaud Doucet, Sumeetpal S. Singh, Vladislav B. Tadic, 2007, Eur. J. Oper. Res..

Random finite sets and sequential Monte Carlo methods in multi-target tracking

Sumeetpal S. Singh, A. Doucet, B. Vo, 2003, 2003 Proceedings of the International Conference on Radar (IEEE Cat. No.03EX695).

Tracking an unknown time-varying number of speakers using TDOA measurements: a random finite set approach

Ba-Ngu Vo, Wing-Kin Ma, Sumeetpal S. Singh, 2006, IEEE Transactions on Signal Processing.

Sequential monte carlo implementation of the phd filter for multi-target tracking

Sumeetpal S. Singh, A. Doucet, B. Vo, 2003, Sixth International Conference of Information Fusion, 2003. Proceedings of the.

Calibrating the Gaussian multi-target tracking model

Sinan Yildirim, Sumeetpal S. Singh, Lan Jiang, 2014, Statistics and Computing.

A Bayesian approach to tracking in single molecule fluorescence microscopy

Sumeetpal S. Singh, J. Yoon, 2008 .

Bayesian Time Series Models: Approximate likelihood estimation of static parameters in multi-target models

Sumeetpal S. Singh, S. Godsill, N. Whiteley, 2011 .

Filtering via approximate Bayesian computation

Sumeetpal S. Singh, Ajay Jasra, James S. Martin, 2010, Statistics and Computing.

The cross-entropy method for blind multiuser detection

Arnaud Doucet, Sumeetpal S. Singh, Zaifei Liu, 2004, International Symposium onInformation Theory, 2004. ISIT 2004. Proceedings..

Convergence of the SMC Implementation of the PHD Filte

Sumeetpal S. Singh, A. Doucet, A. M. Johansen, 2006 .

An Online Expectation–Maximization Algorithm for Changepoint Models

Sumeetpal S. Singh, A. Doucet, S. Yıldırım, 2013 .

On Particle Methods for Parameter Estimation in State-Space Models

Arnaud Doucet, Sumeetpal S. Singh, Nicolas Chopin, 2014, 1412.8695.

Forward Smoothing using Sequential Monte Carlo

Sumeetpal S. Singh, P. Moral, A. Doucet, 2010, 1012.5390.

A backward particle interpretation of Feynman-Kac formulae

Sumeetpal S. Singh, P. Moral, A. Doucet, 2009, 0908.2556.

Particle predictive control

Sumeetpal S. Singh, S. Godsill, J. D. Villiers, 2011 .

Adaptive step-size algorithms for blind interference suppression in DS/CDMA systems

Gang George Yin, Vikram Krishnamurthy, Sumeetpal Singh, 2001, IEEE Trans. Signal Process..

Novel particle filter methods for recursive and batch maximum likelihood parameter estimation in general states space models

Sumeetpal S. Singh, A. Doucet, G. Poyiadjis, 2005 .

Uniform Stability of a Particle Approximation of the Optimal Filter Derivative

Arnaud Doucet, Pierre Del Moral, Sumeetpal S. Singh, 2011, SIAM J. Control. Optim..

Particle Gibbs with refreshed backward simulation

Fredrik Lindsten, Pete Bunch, Sumeetpal Singh, 2015, 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

Blocking strategies and stability of particle Gibbs samplers

Sumeetpal S. Singh, É. Moulines, F. Lindsten, 2015, 1509.08362.

Teaching Machines to Code: Neural Markup Generation with Visual Attention

Sumeet S. Singh, Sumeetpal S. Singh, 2018, 1802.05415.

Approximate Bayesian Computation for Smoothing

Sumeetpal S. Singh, A. Jasra, P. Del Moral, 2012, 1206.5208.

Approximate Smoothing and Parameter Estimation in High-Dimensional State-Space Models

Sumeetpal S. Singh, Axel Finke, 2016, IEEE Transactions on Signal Processing.

Particle ancestor sampling for near-degenerate or intractable state transition models

Sumeetpal S. Singh, F. Lindsten, P. Bunch, 2015, 1505.06356.

Stability of Conditional Sequential Monte Carlo

Sumeetpal S. Singh, Bernd Kuhlenschmidt, Bernd Kuhlenschmidt, 2018, 1806.06520.

Technical aspects of the probability hypothesis density recursion

Ba-Ngu Vo, Adrian Baddeley, Sumeetpal S. Singh, 2005 .

Stochastic approximation for optimal observer trajectory planning

Robin J. Evans, Ba-Ngu Vo, Arnaud Doucet, 2003, 42nd IEEE International Conference on Decision and Control (IEEE Cat. No.03CH37475).

Coupled conditional backward sampling particle filter

Matti Vihola, Anthony Lee, Sumeetpal S. Singh, 2018, The Annals of Statistics.

On the Particle Gibbs Sampler

Sumeetpal S. Singh, N. Chopin, 2013 .

On particle Gibbs sampling

Sumeetpal S. Singh, N. Chopin, 2013, 1304.1887.

Estimating the Static Parameters in Linear Gaussian Multiple Target Tracking Models

Sumeetpal S. Singh, S. Yıldırım, Lan Jiang, 2012, 1212.0849.

Asymptotic behaviour of approximate Bayesian estimators

Thomas A. Dean, Sumeetpal S. Singh, Thomas Dean, 2011, 1105.3655.

ASYMPTOTIC BEHAVIOUR OF APPROXIMATE

Sumeetpal S. Singh, Bayes Estimators, A. Dean, 2011 .

On the loss of Fisher information in some multi-object tracking observation models

Sumeetpal S. Singh, A. Jasra, J. Houssineau, 2018, 1803.09496.

Convergence of the auxiliary particle implementation of the PHD filter

Simon J. Godsill, Nick Whiteley, Sumeetpal S. Singh, 2008 .

Sequential Monte Carlo computation of the score and observed information matrix in state-space models with application to parameter estimation

Arnaud Doucet, Sumeetpal S. Singh, G. Poyiadjis, 2009 .

De-biasing Particle Filtering for a Continuous Time Hidden Markov Model With a Cox Process Observation Model

Sumeetpal S. Singh, N. Chopin, Rui Jin, 2022, Statistica Sinica.

Multilevel Bayesian Deep Neural Networks

Neil K. Chada, Sumeetpal S. Singh, A. Jasra, 2022, ArXiv.

Trace-class Gaussian priors for Bayesian learning of neural networks with MCMC

Sumeetpal S. Singh, Torben Sell, 2020, Journal of the Royal Statistical Society Series B: Statistical Methodology.

On resampling schemes for particle filters with weakly informative observations

Sumeetpal S. Singh, N. Chopin, M. Vihola, 2022, The Annals of Statistics.

Ensemble Kalman inversion for general likelihoods

Sumeetpal S. Singh, Samuel Duffield, 2021, Statistics & Probability Letters.

Quasi-Newton Sequential Monte Carlo

Sumeetpal S. Singh, Samuel Duffield, 2022, 2211.12580.

Conditional particle filters with bridge backward sampling

Sumeetpal S. Singh, M. Vihola, Santeri Karppinen, 2022, 2205.13898.

Calibrating the Gaussian multi-target tracking model

Sumeetpal S. Singh, S. Yıldırım, Lan Jiang, 2014, Statistics and Computing.

Tracking multiple moving objects in images using Markov Chain Monte Carlo

Sumeetpal S. Singh, Lan Jiang, 2017, Statistics and Computing.

Spatiotemporal blocking of the bouncy particle sampler for efficient inference in state-space models

Jacob Vorstrup Goldman, Sumeetpal S. Singh, Sumeetpal Sidhu Singh, 2021, Statistics and Computing.

Particle methods for change detection, system identification, and control

Christophe Andrieu, Arnaud Doucet, Sumeetpal S. Singh, 2004, Proceedings of the IEEE.

Tracking multiple moving objects in images using Markov Chain Monte Carlo

Sumeetpal S. Singh, Lan Jiang, Sumeetpal S. Singh, 2016, Stat. Comput..

A P ] 1 7 M ar 2 01 6 Tracking multiple moving objects in images using Markov Chain

Sumeetpal S. Singh, Lan Jiang, 2016 .

Bayesian Tracking and Parameter Learning for Non-Linear Multiple Target Tracking Models

Sinan Yildirim, Sumeetpal S. Singh, Lan Jiang, 2014, IEEE Transactions on Signal Processing.

A new particle filtering algorithm for multiple target tracking with non-linear observations

Sinan Yildirim, Sumeetpal S. Singh, Lan Jiang, 2014, 17th International Conference on Information Fusion (FUSION).

A Monte Carlo expectation maximisation algorithm for multiple target tracking

Sinan Yildirim, Sumeetpal S. Singh, Lan Jiang, 2012, 2012 15th International Conference on Information Fusion.

An approximate likelihood method for estimating static parameters in multi-target tracking models

Simon J. Godsill, Nick Whiteley, Sumeetpal S. Singh, 2009 .

Chapter 1 An approximate likelihood method for estimating the static parameters in multi-target tracking models

Sumeetpal S. Singh, S. Godsill, N. Whiteley, 2009 .

Probability hypothesis density filter versus multiple hypothesis tracking

Ba-Ngu Vo, Arnaud Doucet, Kusha Panta, 2004, SPIE Defense + Commercial Sensing.

Filtering via approximate Bayesian computation

Sumeetpal S. Singh, A. Jasra, James S. Martin, 2012, Stat. Comput..

Gradient-Based Markov Chain Monte Carlo for Bayesian Inference With Non-differentiable Priors

Jacob Vorstrup Goldman, Sumeetpal S. Singh, Torben Sell, 2021, Journal of the American Statistical Association.

Model-Based Reinforcement Learning for Sepsis Treatment pdf

Matthieu Komorowski, Aniruddh Raghu, Sumeetpal Singh, 2018, ArXiv.

An overview of sequential Monte Carlo methods for parameter estimation in general state-space models

Arnaud Doucet, Jan M. Maciejowski, Nikolas Kantas, 2009 .

Particle methods for optimal filter derivative: application to parameter estimation

Arnaud Doucet, Sumeetpal S. Singh, George Poyiadjis, 2005, Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005..

Anytime Monte Carlo

Anthony Lee, Pierre E. Jacob, Lawrence M. Murray, 2016, Data-Centric Engineering.

Conditional particle filters with bridge backward sampling

Sumeetpal S. Singh, M. Vihola, Santeri Karppinen, 2022, 2205.13898.

Distributed Maximum Likelihood for Simultaneous Self-Localization and Tracking in Sensor Networks

Arnaud Doucet, Sumeetpal S. Singh, Nikolaos Kantas, 2012, IEEE Transactions on Signal Processing.

Integrated voice/data call admission control for wireless DS-CDMA systems

H. Vincent Poor, Vikram Krishnamurthy, Sumeetpal Singh, 2002, IEEE Trans. Signal Process..

On Large Lag Smoothing for Hidden Markov Models

Ajay Jasra, Jeremie Houssineau, Sumeetpal S. Singh, 2018, SIAM J. Numer. Anal..

Simulation-Based Optimal Sensor Scheduling with Application to Observer Trajectory Planning

Robin J. Evans, Ba-Ngu Vo, Arnaud Doucet, 2005, Proceedings of the 44th IEEE Conference on Decision and Control.

De-biasing Particle Filtering for a Continuous Time Hidden Markov Model With a Cox Process Observation Model

Sumeetpal S. Singh, N. Chopin, Rui Jin, 2022, Statistica sinica.

Particle approximations of the score and observed information matrix in state space models with application to parameter estimation

Sumeetpal S. Singh, A. Doucet, G. Poyiadjis, 2011 .

Identification of MultiObject Dynamical Systems: Consistency and Fisher Information

Ajay Jasra, Jeremie Houssineau, Sumeetpal S. Singh, 2017, SIAM J. Control. Optim..

Parameter Estimation in Hidden Markov Models With Intractable Likelihoods Using Sequential Monte Carlo

Ajay Jasra, Sinan Yildirim, Sumeetpal Singh, 2013, 1311.4117.

Esaim: Mathematical Modelling and Numerical Analysis a Backward Particle Interpretation of Feynman-kac Formulae

Sumeetpal S. Singh, P. Moral, A. Doucet, 2022 .

Bayesian Learning of Noisy Markov Decision Processes

Nick Whiteley, Sumeetpal S. Singh, Nicolas Chopin, 2012, TOMC.

Sequential Monte Carlo methods for static parameter estimation in random set models

Ba-Ngu Vo, Ba-Tuong Vo, Sumeetpal Singh, 2004, Proceedings of the 2004 Intelligent Sensors, Sensor Networks and Information Processing Conference, 2004..

A backward Itô–Ventzell formula with an application to stochastic interpolation

Sumeetpal S. Singh, P. Moral, 2020, Comptes Rendus. Mathématique.

Convergence of the SMC Implementation of the PHD Filte

Sumeetpal S. Singh, A. Doucet, A. M. Johansen, 2006 .

Backward Itô-Ventzell and stochastic interpolation formulae

Sumeetpal S. Singh, P. Moral, P. Del Moral, 2022, Stochastic Processes and their Applications.

Multilevel Monte Carlo for Smoothing via Transport Methods

Sumeetpal S. Singh, Ajay Jasra, Jeremie Houssineau, 2017, SIAM J. Sci. Comput..

On Particle Methods for Parameter Estimation in General State-Space Models

Jan M. Maciejowski, Sumeetpal S. Singh, Nicolas Chopin, 2015 .

Sequential paging of mobile users in GSM cellular networks-a POMDP approach

Vikram Krishnamurthy, Sumeetpal Singh, Sumeetpal S. Singh, 2000, Globecom '00 - IEEE. Global Telecommunications Conference. Conference Record (Cat. No.00CH37137).

Filters for Spatial Point Processes

Ba-Ngu Vo, Sumeetpal S. Singh, Sergei A. Zuyev, 2009, SIAM J. Control. Optim..