Sequential Monte Carlo Methods in Practice
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[1] N. Metropolis,et al. Equation of State Calculations by Fast Computing Machines , 1953, Resonance.
[2] J. Hammersley,et al. Poor Man's Monte Carlo , 1954 .
[3] A. W. Rosenbluth,et al. MONTE CARLO CALCULATION OF THE AVERAGE EXTENSION OF MOLECULAR CHAINS , 1955 .
[4] F. T. Wall,et al. New Method for the Statistical Computation of Polymer Dimensions , 1959 .
[5] Maurice G. Kendall,et al. The advanced theory of statistics , 1945 .
[6] K. Parthasarathy,et al. Probability measures on metric spaces , 1967 .
[7] Van Trees,et al. Detection, Estimation, and Modulation Theory. Part 1 - Detection, Estimation, and Linear Modulation Theory. , 1968 .
[8] Iosif Ilitch Gikhman,et al. Introduction to the theory of random processes , 1969 .
[9] D. Mayne,et al. Monte Carlo techniques to estimate the conditional expectation in multi-stage non-linear filtering† , 1969 .
[10] W. K. Hastings,et al. Monte Carlo Sampling Methods Using Markov Chains and Their Applications , 1970 .
[11] M. Degroot. Optimal Statistical Decisions , 1970 .
[12] H. Sorenson,et al. Recursive bayesian estimation using gaussian sums , 1971 .
[13] H. Sorenson,et al. Nonlinear Bayesian estimation using Gaussian sum approximations , 1972 .
[14] D. Titterington. A Method of Extremum Adaptation , 1973 .
[15] Y. Bar-Shalom,et al. Dual effect, certainty equivalence, and separation in stochastic control , 1974 .
[16] R. Azzam,et al. Ellipsometry and polarized light , 1977 .
[17] M. Netto,et al. On the optimal and suboptimal nonlinear filtering problem for discrete-time systems , 1977 .
[18] Y. Bar-Shalom. Stochastic dynamic programming: Caution and probing , 1981 .
[19] A F Smith,et al. Monitoring renal transplants: an application of the multiprocess Kalman filter. , 1983, Biometrics.
[20] Yaakov Bar-Shalom,et al. Dual control guidance for simultaneous identification and interception , 1983, The 22nd IEEE Conference on Decision and Control.
[21] Graham C. Goodwin,et al. Adaptive filtering prediction and control , 1984 .
[22] M. West,et al. Dynamic Generalized Linear Models and Bayesian Forecasting , 1985 .
[23] G. Kitagawa,et al. A smoothness priors time-varying AR coefficient modeling of nonstationary covariance time series , 1985, IEEE Transactions on Automatic Control.
[24] Peter Andersson,et al. Adaptive Forgetting in Recursive Identification through Multiple Models , 1985 .
[25] H. Meirovitch. Scanning method with a mean-field parameter: computer simulation study of critical exponents of self-avoiding walks on a square lattice , 1985 .
[26] Hans P. Moravec,et al. High resolution maps from wide angle sonar , 1985, Proceedings. 1985 IEEE International Conference on Robotics and Automation.
[27] S. Geman,et al. Diffusions for global optimizations , 1986 .
[28] M. West. Bayesian Model Monitoring , 1986 .
[29] F. Diebold,et al. The dynamics of exchange rate volatility: a multivariate latent factor ARCH model , 1986 .
[30] A. F. Smith,et al. Statistical analysis of finite mixture distributions , 1986 .
[31] Michael A. West,et al. Monitoring and Adaptation in Bayesian Forecasting Models , 1986 .
[32] L. Rabiner,et al. An introduction to hidden Markov models , 1986, IEEE ASSP Magazine.
[33] G. Kitagawa,et al. Smoothness Priors in Time Series. , 1987 .
[34] Dimitri P. Bertsekas,et al. Dynamic Programming: Deterministic and Stochastic Models , 1987 .
[35] Lennart Ljung,et al. System Identification: Theory for the User , 1987 .
[36] L. Devroye. A Course in Density Estimation , 1987 .
[37] G. Kitagawa. Non-Gaussian State—Space Modeling of Nonstationary Time Series , 1987 .
[38] James E. Baker,et al. Reducing Bias and Inefficienry in the Selection Algorithm , 1987, ICGA.
[39] P. Diaconis,et al. The Subgroup Algorithm for Generating Uniform Random Variables , 1987, Probability in the Engineering and Informational Sciences.
[40] Alan G. White,et al. The Pricing of Options on Assets with Stochastic Volatilities , 1987 .
[41] Y. Bar-Shalom,et al. The interacting multiple model algorithm for systems with Markovian switching coefficients , 1988 .
[42] Mark S. Boddy,et al. An Analysis of Time-Dependent Planning , 1988, AAAI.
[43] Kurt Kremer,et al. Monte Carlo simulation of lattice models for macromolecules , 1988 .
[44] David E. Goldberg,et al. Genetic Algorithms in Search Optimization and Machine Learning , 1988 .
[45] Amir Averbuch,et al. Interacting Multiple Model Methods in Target Tracking: A Survey , 1988 .
[46] S. Zeger. A regression model for time series of counts , 1988 .
[47] Y. Bar-Shalom. Tracking and data association , 1988 .
[48] Stuart German,et al. Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images , 1988 .
[49] D. Bayard. A forward method for optimal stochastic nonlinear and adaptive control , 1988, Proceedings of the 27th IEEE Conference on Decision and Control.
[50] W. Cleveland,et al. Locally Weighted Regression: An Approach to Regression Analysis by Local Fitting , 1988 .
[51] T. Kerr. Status of CR-like lower bounds for nonlinear filtering , 1989 .
[52] Y. Bar-Shalom,et al. Tracking a maneuvering target using input estimation versus the interacting multiple model algorithm , 1989 .
[53] J. Geweke,et al. BAYESIAN INFERENCE IN ECONOMETRIC MODELS USING , 1989 .
[54] Keiji Kanazawa,et al. A model for reasoning about persistence and causation , 1989 .
[55] R. Hinkel,et al. ENVIRONMENT PERCEPTION WITH A LASER RADAR IN A FAST MOVING ROBOT , 1989 .
[56] D. Rubin,et al. Multiple Imputation for Nonresponse in Surveys , 1989 .
[57] C. Robert Kenley,et al. Gaussian influence diagrams , 1989 .
[58] Heinrich Meyr,et al. A systematic approach to carrier recovery and detection of digitally phase modulated signals of fading channels , 1989, IEEE Trans. Commun..
[59] Jeff Harrison,et al. Subjective intervention in formal models , 1989 .
[60] F. Girosi,et al. Networks for approximation and learning , 1990, Proc. IEEE.
[61] Kumpati S. Narendra,et al. Identification and control of dynamical systems using neural networks , 1990, IEEE Trans. Neural Networks.
[62] G. C. Wei,et al. A Monte Carlo Implementation of the EM Algorithm and the Poor Man's Data Augmentation Algorithms , 1990 .
[63] Michael A. West,et al. Efficient bayesian learning in non‐linear dynamic models , 1990 .
[64] Randall Smith,et al. Estimating Uncertain Spatial Relationships in Robotics , 1987, Autonomous Robot Vehicles.
[65] D. Hull,et al. Linear-quadratic guidance law for dual control of homing missiles , 1990 .
[66] Stephen M. Omohundro,et al. Bumptrees for Efficient Function, Constraint and Classification Learning , 1990, NIPS.
[67] A. L. Sutherland,et al. Finding spiral structures in images of galaxies , 1990, Philosophical Transactions of the Royal Society of London. Series A: Physical and Engineering Sciences.
[68] John H. Lodge,et al. Maximum likelihood sequence estimation of CPM signals transmitted over Rayleigh flat-fading channels , 1990, IEEE Trans. Commun..
[69] L. Fahrmeir,et al. On kalman filtering, posterior mode estimation and fisher scoring in dynamic exponential family regression , 1991 .
[70] David Williams,et al. Probability with Martingales , 1991, Cambridge mathematical textbooks.
[71] Ingemar J. Cox,et al. Blanche-an experiment in guidance and navigation of an autonomous robot vehicle , 1991, IEEE Trans. Robotics Autom..
[72] J J Koenderink,et al. Affine structure from motion. , 1991, Journal of the Optical Society of America. A, Optics and image science.
[73] G. Kitagawa. A nonlinear smoothing method for time series analysis , 1991 .
[74] J. Cavers. An analysis of pilot symbol assisted modulation for Rayleigh fading channels (mobile radio) , 1991 .
[75] Georg Lindgren,et al. Recursive estimation in mixture models with Markov regime , 1991, IEEE Trans. Inf. Theory.
[76] Amir Averbuch,et al. Radar target tracking-Viterbi versus IMM , 1991 .
[77] Ronen Basri,et al. Recognition by Linear Combinations of Models , 1991, IEEE Trans. Pattern Anal. Mach. Intell..
[78] L. Fahrmeir. Posterior Mode Estimation by Extended Kalman Filtering for Multivariate Dynamic Generalized Linear Models , 1992 .
[79] A. Harvey,et al. Unobserved component time series models with Arch disturbances , 1992 .
[80] N. Kashiwagi,et al. Smoothing serial count data through a state-space model , 1992 .
[81] Nicholas G. Polson,et al. A Monte Carlo Approach to Nonnormal and Nonlinear State-Space Modeling , 1992 .
[82] Alan E. Gelfand,et al. Bayesian statistics without tears: A sampling-resampling perspective , 1992 .
[83] Sylvia Schnatter. Integration-based Kalman-filtering for a dynamic generalized linear trend model , 1992 .
[84] Drew McDermott,et al. Error correction in mobile robot map learning , 1992, Proceedings 1992 IEEE International Conference on Robotics and Automation.
[85] Pooi Yuen Kam,et al. Sequence Estimation over the Slow Nonselective Rayleigh Fading Channel with Diversity Reception and Its Application to Viterbi Decoding , 1992, IEEE J. Sel. Areas Commun..
[86] Ingemar J. Cox,et al. Dynamic Map Building for an Autonomous Mobile Robot , 1992 .
[87] Paul Dagum,et al. Forecasting Sleep Apnea with Dynamic Network Models , 1993, UAI.
[88] Martin Abba Tanner,et al. Tools for Statistical Inference: Observed Data and Data Augmentation Methods , 1993 .
[89] N. Gordon,et al. Novel approach to nonlinear/non-Gaussian Bayesian state estimation , 1993 .
[90] J. Besag,et al. Spatial Statistics and Bayesian Computation , 1993 .
[91] Richard L. Tweedie,et al. Markov Chains and Stochastic Stability , 1993, Communications and Control Engineering Series.
[92] S. Ito,et al. Navigation system based on ceiling landmark recognition for autonomous mobile robot , 1993, Proceedings of IECON '93 - 19th Annual Conference of IEEE Industrial Electronics.
[93] Timothy F. Cootes,et al. Building and using flexible models incorporating grey-level information , 1993, 1993 (4th) International Conference on Computer Vision.
[94] Hisashi Tanizaki,et al. Nonlinear filters , 1993 .
[95] Neil Gordon,et al. Bayesian methods for tracking , 1993 .
[96] S. Sampei,et al. Rayleigh fading compensation for QAM in land mobile radio communications , 1993 .
[97] R. White,et al. Image recovery from data acquired with a charge-coupled-device camera. , 1993, Journal of the Optical Society of America. A, Optics and image science.
[98] Wolfgang D. Rencken,et al. Concurrent localisation and map building for mobile robots using ultrasonic sensors , 1993, Proceedings of 1993 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS '93).
[99] A. Kong,et al. Sequential imputation for multilocus linkage analysis. , 1994, Proceedings of the National Academy of Sciences of the United States of America.
[100] Lee A. Feldkamp,et al. Neurocontrol of nonlinear dynamical systems with Kalman filter trained recurrent networks , 1994, IEEE Trans. Neural Networks.
[101] Jun S. Liu,et al. Sequential Imputations and Bayesian Missing Data Problems , 1994 .
[102] S. Frühwirth-Schnatter. Applied state space modelling of non-Gaussian time series using integration-based Kalman filtering , 1994 .
[103] Geir Storvik,et al. A Bayesian Approach to Dynamic Contours Through Stochastic Sampling and Simulated Annealing , 1994, IEEE Trans. Pattern Anal. Mach. Intell..
[104] John B. Moore,et al. AN HMM APPROACH TO ADAPTIVE DEMODULATION OF QAM SIGNALS IN FADING CHANNELS , 1994 .
[105] N. Shephard. Partial non-Gaussian state space , 1994 .
[106] Jun S. Liu,et al. Covariance structure of the Gibbs sampler with applications to the comparisons of estimators and augmentation schemes , 1994 .
[107] Peter E. Rossi,et al. Bayesian Analysis of Stochastic Volatility Models , 1994 .
[108] Ann E. Nicholson,et al. Dynamic Belief Networks for Discrete Monitoring , 1994, IEEE Trans. Syst. Man Cybern. Syst..
[109] C. Geyer. Estimating Normalizing Constants and Reweighting Mixtures , 1994 .
[110] Michael Isard,et al. 3D position, attitude and shape input using video tracking of hands and lips , 1994, SIGGRAPH.
[111] W. Dale Blair,et al. Interacting multiple model algorithm for solution to benchmark problem for tracking maneuvering targets , 1994, Defense, Security, and Sensing.
[112] Enrique Sentana,et al. Volatiltiy and Links between National Stock Markets , 1990 .
[113] R. Kohn,et al. On Gibbs sampling for state space models , 1994 .
[114] Jitendra Malik,et al. Automatic Symbolic Traffic Scene Analysis Using Belief Networks , 1994, AAAI.
[115] David C. Hogg,et al. Learning Flexible Models from Image Sequences , 1994, ECCV.
[116] Anup Basu,et al. Motion Tracking with an Active Camera , 1994, IEEE Trans. Pattern Anal. Mach. Intell..
[117] L. Tierney. Markov Chains for Exploring Posterior Distributions , 1994 .
[118] Ewald von Puttkamer,et al. Keeping track of position and orientation of moving indoor systems by correlation of range-finder scans , 1994, Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS'94).
[119] N. Shephard,et al. Stochastic Volatility: Likelihood Inference And Comparison With Arch Models , 1996 .
[120] Jitendra Malik,et al. Robust Multiple Car Tracking with Occlusion Reasoning , 1994, ECCV.
[121] Robert M. Fung,et al. Backward Simulation in Bayesian Networks , 1994, UAI.
[122] John B. Moore,et al. An adaptive hidden Markov model approach to FM and M-ary DPSK demodulation in noisy fading channels , 1995, Signal Process..
[123] Stuart J. Russell,et al. Stochastic simulation algorithms for dynamic probabilistic networks , 1995, UAI.
[124] D. Avitzour. Stochastic simulation Bayesian approach to multitarget tracking , 1995 .
[125] Aaron F. Bobick,et al. Recognition of human body motion using phase space constraints , 1995, Proceedings of IEEE International Conference on Computer Vision.
[126] Neil J. Gordon,et al. Bayesian State Estimation for Tracking and Guidance Using the Bootstrap Filter , 1993 .
[127] G. Tanner,et al. Missile control against multiple targets using non-quadratic cost functions , 1995, Proceedings of 1995 American Control Conference - ACC'95.
[128] David Beymer,et al. Face recognition from one example view , 1995, Proceedings of IEEE International Conference on Computer Vision.
[129] Reid G. Simmons,et al. Probabilistic Robot Navigation in Partially Observable Environments , 1995, IJCAI.
[130] Shlomo Zilberstein,et al. Approximate Reasoning Using Anytime Algorithms , 1995 .
[131] P. Doerschuk. Cramer-Rao bounds for discrete-time nonlinear filtering problems , 1995, IEEE Trans. Autom. Control..
[132] P. Green. Reversible jump Markov chain Monte Carlo computation and Bayesian model determination , 1995 .
[133] T. Hesterberg,et al. Weighted Average Importance Sampling and Defensive Mixture Distributions , 1995 .
[134] K. Chan,et al. Monte Carlo EM Estimation for Time Series Models Involving Counts , 1995 .
[135] Dimitri P. Bertsekas,et al. Dynamic Programming and Optimal Control, Two Volume Set , 1995 .
[136] Chris J. Harris,et al. Multi-sensor data fusion for helicopter guidance using neuro-fuzzy estimation algorithms , 1995, 1995 IEEE International Conference on Systems, Man and Cybernetics. Intelligent Systems for the 21st Century.
[137] J. M. Torrance,et al. Comparative study of pilot symbol assisted modem schemes , 1995 .
[138] U. Grenander,et al. nal-Mean Estimation Via Jump-Diffusion ses in Multiple Target Tracking/Recognition , 1995 .
[139] Timothy F. Cootes,et al. A unified approach to coding and interpreting face images , 1995, Proceedings of IEEE International Conference on Computer Vision.
[140] Desmond P. Taylor,et al. Maximum likelihood decoding of uncoded and coded PSK signal sequences transmitted over Rayleigh flat-fading channels , 1995, IEEE Trans. Commun..
[141] J. Geweke,et al. Measuring the pricing error of the arbitrage pricing theory , 1996 .
[142] Jun S. Liu,et al. Blind Deconvolution via Sequential Imputations , 1995 .
[143] Steven D. Blostein,et al. Identification of frequency non-selective fading channels using decision feedback and adaptive linear prediction , 1995, IEEE Trans. Commun..
[144] David C. Hogg,et al. An Adaptive Eigenshape Model , 1995, BMVC.
[145] Michael Isard,et al. Learning to Track the Visual Motion of Contours , 1995, Artif. Intell..
[146] Michael P. Fitz,et al. Near-optimal symbol-by-symbol detection schemes for flat Rayleigh fading , 1995, IEEE Trans. Commun..
[147] D. Talay,et al. The law of the Euler scheme for stochastic differential equations , 1996 .
[148] N. Stenseth,et al. Is spacing behaviour coupled with predation causing the micro tine density cycle? A synthesis of current process-oriented and pattern-oriented studies , 1996, Proceedings of the Royal Society of London. Series B: Biological Sciences.
[149] G. Kitagawa. Monte Carlo Filter and Smoother for Non-Gaussian Nonlinear State Space Models , 1996 .
[150] A. Raftery,et al. Local Adaptive Importance Sampling for Multivariate Densities with Strong Nonlinear Relationships , 1996 .
[151] Andrew Blake,et al. Statistical mosaics for tracking , 1996, Image Vis. Comput..
[152] Craig Boutilier,et al. Context-Specific Independence in Bayesian Networks , 1996, UAI.
[153] Tomaso A. Poggio,et al. Image Synthesis from a Single Example Image , 1996, ECCV.
[154] Wolfram Burgard,et al. Estimating the Absolute Position of a Mobile Robot Using Position Probability Grids , 1996, AAAI/IAAI, Vol. 2.
[155] David C. Hogg,et al. Generating Spatiotemporal Models from Examples , 1995, BMVC.
[156] Norikazu Ikoma. Estimation of time varying peak of power spectrum based on non-Gaussian nonlinear state space modeling , 1996, Signal Process..
[157] Reid G. Simmons,et al. Passive Distance Learning for Robot Navigation , 1996, ICML.
[158] C. Russell,et al. Detection and Behavior of Pan Wakes in Saturn's A Ring , 1996 .
[159] Liqiang Feng,et al. Navigating Mobile Robots: Systems and Techniques , 1996 .
[160] R. Kohn,et al. Markov chain Monte Carlo in conditionally Gaussian state space models , 1996 .
[161] Michael Isard,et al. Contour Tracking by Stochastic Propagation of Conditional Density , 1996, ECCV.
[162] Gordon L. Stuber,et al. Principles of Mobile Communication , 1996 .
[163] Leslie Pack Kaelbling,et al. Acting under uncertainty: discrete Bayesian models for mobile-robot navigation , 1996, Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems. IROS '96.
[164] D. Mumford. Pattern theory: a unifying perspective , 1996 .
[165] Peter J Green,et al. Markov chain Monte Carlo in image analysis , 1996 .
[166] N. Stenseth,et al. A gradient from stable to cyclic populations of Clethrionomys rufocanus in Hokkaido, Japan , 1996, Proceedings of the Royal Society of London. Series B: Biological Sciences.
[167] Andrew Blake,et al. Learning Dynamics of Complex Motions from Image Sequences , 1996, ECCV.
[168] Anthony G. Cohn,et al. Generation of Semantic Regions from Image Sequences , 1996, ECCV.
[169] Jun S. Liu. Nonparametric hierarchical Bayes via sequential imputations , 1996 .
[170] J. Durbin,et al. Monte Carlo maximum likelihood estimation for non-Gaussian state space models , 1997 .
[171] Michael J. Gertsman,et al. Symbol-by-symbol MAP demodulation of CPM and PSK signals on Rayleigh flat-fading channels , 1997, IEEE Trans. Commun..
[172] Costas N. Georghiades,et al. Sequence estimation in the presence of random parameters via the EM algorithm , 1997, IEEE Trans. Commun..
[173] J. J. Rajan,et al. Bayesian approach to parameter estimation and interpolation of time-varying autoregressive processes using the Gibbs sampler , 1997 .
[174] P. Grassberger. Pruned-enriched Rosenbluth method: Simulations of θ polymers of chain length up to 1 000 000 , 1997 .
[175] Simon J. Godsill,et al. Bayesian Enhancement of Speech and Audio Signals which can be Modelled as ARMA Processes , 1997 .
[176] Michael I. Miller,et al. Accommodating geometric and thermodynamic variability for forward-looking infrared sensors , 1997, Defense, Security, and Sensing.
[177] T. Higuchi. Monte carlo filter using the genetic algorithm operators , 1997 .
[178] T. Rydén. On recursive estimation for hidden Markov models , 1997 .
[179] R. Tweedie,et al. Exponential Convergence of Langevin Diiusions and Their Discrete Approximations , 1997 .
[180] Jun S. Liu,et al. Sequential Monte Carlo methods for dynamic systems , 1997 .
[181] N. Stenseth,et al. Population regulation in snowshoe hare and Canadian lynx: asymmetric food web configurations between hare and lynx. , 1997, Proceedings of the National Academy of Sciences of the United States of America.
[182] M. Pitt,et al. Likelihood analysis of non-Gaussian measurement time series , 1997 .
[183] Michael I. Miller,et al. General Metropolis-Hastings jump diffusions for automatic target recognition in infrared scenes , 1997 .
[184] N. Gordon. A hybrid bootstrap filter for target tracking in clutter , 1995, IEEE Transactions on Aerospace and Electronic Systems.
[185] Aaron F. Bobick,et al. State-Based Recognition of Gesture , 1997 .
[186] N. G. Best,et al. Dynamic conditional independence models and Markov chain Monte Carlo methods , 1997 .
[187] Michael I. Jordan,et al. Probabilistic Independence Networks for Hidden Markov Probability Models , 1997, Neural Computation.
[188] Karen Zita Haigh,et al. A layered architecture for office delivery robots , 1997, AGENTS '97.
[189] J. Monahan,et al. Spherical-Radial Integration Rules for Bayesian Computation , 1997 .
[190] D. Crisan,et al. Nonlinear filtering and measure-valued processes , 1997 .
[191] Andrew W. Moore,et al. Efficient Locally Weighted Polynomial Regression Predictions , 1997, ICML.
[192] Carlos H. Muravchik,et al. Posterior Cramer-Rao bounds for discrete-time nonlinear filtering , 1998, IEEE Trans. Signal Process..
[193] Simon J. Godsill,et al. On sequential simulation-based methods for Bayesian filtering , 1998 .
[194] Wolfram Burgard,et al. A Probabilistic Approach to Concurrent Mapping and Localization for Mobile Robots , 1998, Auton. Robots.
[195] M. Pitt,et al. Time Varying Covariances: A Factor Stochastic Volatility Approach (with discussion , 1998 .
[196] Pierre Del Moral,et al. Discrete Filtering Using Branching and Interacting Particle Systems , 1998 .
[197] Geoffrey Zweig,et al. Speech Recognition with Dynamic Bayesian Networks , 1998, AAAI/IAAI.
[198] Andrew Blake,et al. Separability of pose and expression in facial tracking and animation , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).
[199] Duncan Fyfe Gillies,et al. Deformable models for object recognition in aerial images , 1998, Defense, Security, and Sensing.
[200] Michael Isard,et al. A mixed-state condensation tracker with automatic model-switching , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).
[201] M. Isard,et al. Statistical models of visual shape and motion , 1998, Philosophical Transactions of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.
[202] Simon J. Godsill,et al. Statistical reconstruction and analysis of autoregressive signals in impulsive noise using the Gibbs sampler , 1998, IEEE Trans. Speech Audio Process..
[203] C. C. Homes,et al. Bayesian Radial Basis Functions of Variable Dimension , 1998, Neural Computation.
[204] Wolfram Burgard,et al. Active Markov localization for mobile robots , 1998, Robotics Auton. Syst..
[205] Andrew Blake,et al. A probabilistic contour discriminant for object localisation , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).
[206] Alain Monfort,et al. The Simulated Likelihood Ratio (SLR) Method , 1998 .
[207] Robin R. Murphy,et al. Artificial intelligence and mobile robots: case studies of successful robot systems , 1998 .
[208] C. Pickering. Complementary in-situ and post-deposition diagnostics of thin film semiconductor structures , 1998 .
[209] Xavier Boyen,et al. Tractable Inference for Complex Stochastic Processes , 1998, UAI.
[210] G. Peters,et al. Monte Carlo Approximations for General State-Space Models , 1998 .
[211] Donka Angelova,et al. Target tracking using Monte Carlo simulation , 1998 .
[212] G. Oehlert. Faster Adaptive Importance Sampling in Low Dimensions , 1998 .
[213] P. Bickel,et al. Asymptotic normality of the maximum-likelihood estimator for general hidden Markov models , 1998 .
[214] Jun S. Liu,et al. Rejection Control and Sequential Importance Sampling , 1998 .
[215] Richard V. Lawrence. Interceptor Line-of-Sight Rate Steering: Necessary Conditions for a Direct Hit , 1998 .
[216] Daphne Koller,et al. Using Learning for Approximation in Stochastic Processes , 1998, ICML.
[217] Markus Hürzeler. Statistical methods for general state-space models , 1998 .
[218] Peter J. W. Rayner,et al. Digital Audio Restoration: A Statistical Model Based Approach , 1998 .
[219] Michael I. Miller,et al. Hilbert-Schmidt Lower Bounds for Estimators on Matrix Lie Groups for ATR , 1998, IEEE Trans. Pattern Anal. Mach. Intell..
[220] Neil J. Gordon,et al. Tracking in the presence of intermittent spurious objects and clutter , 1998, Defense, Security, and Sensing.
[221] Fredrik Gustafsson,et al. Terrain navigation using Bayesian statistics , 1999 .
[222] Michael I. Miller,et al. Estimation of pose and location of ground targets for ATR , 1999, Defense, Security, and Sensing.
[223] Kevin P. Murphy,et al. Bayesian Map Learning in Dynamic Environments , 1999, NIPS.
[224] Wolfram Burgard,et al. Using the CONDENSATION algorithm for robust, vision-based mobile robot localization , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).
[225] P. Fearnhead,et al. Improved particle filter for nonlinear problems , 1999 .
[226] Wolfram Burgard,et al. Monte Carlo Localization: Efficient Position Estimation for Mobile Robots , 1999, AAAI/IAAI.
[227] Andrew Blake,et al. Using expectation-maximisation to learn dynamical models from visual data , 1999, Image Vis. Comput..
[228] Jun S. Liu,et al. Sequential importance sampling for nonparametric Bayes models: The next generation , 1999 .
[229] M. Pitt,et al. Filtering via Simulation: Auxiliary Particle Filters , 1999 .
[230] W. Burgard,et al. Markov Localization for Mobile Robots in Dynamic Environments , 1999, J. Artif. Intell. Res..
[231] S. Thrun,et al. Mosaicing a Large Number of Widely Dispersed, Noisy, and Distorted Images: A Bayesian Approach , 1999 .
[232] Dragomir Anguelov,et al. A General Algorithm for Approximate Inference and Its Application to Hybrid Bayes Nets , 1999, UAI.
[233] Simon J. Godsill,et al. Fixed-lag smoothing using sequential importance sampling , 1999 .
[234] P. Moral,et al. Central limit theorem for nonlinear filtering and interacting particle systems , 1999 .
[235] Michael A. West,et al. Evaluation and Comparison of EEG Traces: Latent Structure in Nonstationary Time Series , 1999 .
[236] Christophe Andrieu,et al. Sequential Bayesian Estimation And Model Selection Applied To Neural Networks , 1999 .
[237] P.M. Djuric,et al. Monitoring and selection of dynamic models by Monte Carlo sampling , 1999, Proceedings of the IEEE Signal Processing Workshop on Higher-Order Statistics. SPW-HOS '99.
[238] P. Vidoni. Exponential family state space models based on a conjugate latent process , 1999 .
[239] R. Kohn,et al. Diagnostics for Time Series Analysis , 1999 .
[240] Wolfram Burgard,et al. Monte Carlo localization for mobile robots , 1999, Proceedings 1999 IEEE International Conference on Robotics and Automation (Cat. No.99CH36288C).
[241] Xavier Boyen,et al. Exploiting the Architecture of Dynamic Systems , 1999, AAAI/IAAI.
[242] Wolfram Burgard,et al. MINERVA: a second-generation museum tour-guide robot , 1999, Proceedings 1999 IEEE International Conference on Robotics and Automation (Cat. No.99CH36288C).
[243] N. Oudjane,et al. Multiple model particle filter , 1999 .
[244] T. Higuchi. Applications of quasi-periodic oscillation models to seasonal small count time series , 1999 .
[245] Kevin P. Murphy,et al. A Variational Approximation for Bayesian Networks with Discrete and Continuous Latent Variables , 1999, UAI.
[246] Pieter J. Mosterman,et al. Diagnosis of continuous valued systems in transient operating regions , 1999, IEEE Trans. Syst. Man Cybern. Part A.
[247] Wolfram Burgard,et al. Monte Carlo Localization with Mixture Proposal Distribution , 2000, AAAI/IAAI.
[248] Simon J. Godsill,et al. On-line Bayesian modelling and enhancement of speech signals , 2000 .
[249] G. Kitagawa,et al. NONLINEAR STATE SPACE MODEL APPROACH TO FINANCIAL TIME SERIES WITH TIME-VARYING VARIANCE , 2000 .
[250] Gomes de Freitas,et al. Bayesian methods for neural networks , 2000 .
[251] Simon J. Godsill,et al. Methodology for Monte Carlo smoothing with application to time-varying autoregressions , 2000 .
[252] Petar M. Djuric,et al. Sequential Monte Carlo sampling detector for Rayleigh fast-fading channels , 2000, 2000 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.00CH37100).
[253] Simon J. Godsill,et al. On sequential Monte Carlo sampling methods for Bayesian filtering , 2000, Stat. Comput..
[254] Nando de Freitas,et al. The Unscented Particle Filter , 2000, NIPS.
[255] Manuela M. Veloso,et al. Sensor resetting localization for poorly modelled mobile robots , 2000, Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065).
[256] Anuj Srivastava,et al. Bayesian filtering for tracking pose and location of rigid targets , 2000, SPIE Defense + Commercial Sensing.
[257] Hans Kiinsch,et al. State Space and Hidden Markov Models , 2000 .
[258] M. West,et al. Bayesian Dynamic Factor Models and Portfolio Allocation , 2000 .
[259] Nando de Freitas,et al. Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks , 2000, UAI.
[260] M. Ledoux,et al. Convergence of Empirical Processes for Interacting Particle Systems with Applications to Nonlinear Filtering , 2000 .
[261] Arnaud Doucet,et al. Sequential Monte Carlo Methods to Train Neural Network Models , 2000, Neural Computation.
[262] P. Moral,et al. Branching and interacting particle systems. Approximations of Feynman-Kac formulae with applications to non-linear filtering , 2000 .
[263] Wolfram Burgard,et al. A Probabilistic Approach to Collaborative Multi-Robot Localization , 2000, Auton. Robots.
[264] Anuj Srivastava,et al. Probability Models for Clutter in Natural Images , 2001, IEEE Trans. Pattern Anal. Mach. Intell..
[265] W. Gilks,et al. Following a moving target—Monte Carlo inference for dynamic Bayesian models , 2001 .
[266] Arnaud Doucet,et al. Particle filters for state estimation of jump Markov linear systems , 2001, IEEE Trans. Signal Process..
[267] P. Protter,et al. The Monte-Carlo method for filtering with discrete-time observations , 2001 .
[268] T. Başar,et al. A New Approach to Linear Filtering and Prediction Problems , 2001 .
[269] Dan Crisan,et al. Minimal Entropy Approximations and Optimal Algorithms , 2002, Monte Carlo Methods Appl..
[270] G. Lewicki,et al. Approximation by Superpositions of a Sigmoidal Function , 2003 .
[271] S. Zacks,et al. Journal of Statistical Planning and Inference , 2016 .