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ORCIDSimo Särkkä
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- affiliation: Aalto University, Finland
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2020 – today
- 2024
[j82]Nathanael Bosch, Adrien Corenflos, Fatemeh Yaghoobi, Filip Tronarp, Philipp Hennig, Simo Särkkä:
Parallel-in-Time Probabilistic Numerical ODE Solvers. J. Mach. Learn. Res. 25: 206:1-206:27 (2024)- [j81]Muhammad F. Emzir
, Zheng Zhao, Lahouari Cheded, Simo Särkkä:
Gaussian-Based Parametric Bijections for Automatic Projection Filters. IEEE Trans. Autom. Control. 69(5): 3449-3456 (2024) - [j80]Tabish Badar, Simo Särkkä, Zheng Zhao, Arto Visala:
Rao-Blackwellized Particle Filter Using Noise Adaptive Kalman Filter for Fully Mixing State-Space Models. IEEE Trans. Aerosp. Electron. Syst. 60(5): 6972-6982 (2024) - [j79]Zaeed Khan, Matias Rusanen, Miika Arvonen, Timo Leppänen, Simo Särkkä:
Joint Use of a Low Thermal Resolution Thermal Camera and an RGB Camera for Respiration Measurement. IEEE Trans. Instrum. Meas. 73: 1-14 (2024) - [j78]Xiaofeng Ma, Simo Särkkä:
Spacing Vector and Varying Distance Constrained Positioning Using Dual Feet-Mounted IMUs. IEEE Trans. Instrum. Meas. 73: 1-11 (2024) - [c93]Kundan Kumar, Simo Särkkä:
Polynomial Chaos Expansion Based Rauch-Tung-Striebel Smoothers. FUSION 2024: 1-7 - [c92]Matti Raitoharju, Ángel F. García-Fernández, Simo Ali-Löytty, Simo Särkkä:
Stacked iterated posterior linearization filter. FUSION 2024: 1-8 - [c91]Christos Merkatas, Simo Särkkä:
A Gibbs Sampler for Bayesian Nonparametric State-Space Models. ICASSP 2024: 13236-13240 - [c90]Sahel Iqbal, Adrien Corenflos, Simo Särkkä, Hany Abdulsamad:
Nesting Particle Filters for Experimental Design in Dynamical Systems. ICML 2024 - [c89]Kundan Kumar, Muhammad Iqbal, Simo Särkkä:
Risk-Sensitive Filtering under False Data Injection Attacks. MFI 2024: 1-6 - [c88]Sahel Iqbal, Hany Abdulsamad, Tripp Cator, Ulisses Braga-Neto, Simo Särkkä:
Parallel-in-Time Probabilistic Solutions for Time-Dependent Nonlinear Partial Differential Equations. MLSP 2024: 1-6 - [i59]Yvann Le Fay, Simo Särkkä, Adrien Corenflos:
Modelling pathwise uncertainty of Stochastic Differential Equations samplers via Probabilistic Numerics. CoRR abs/2401.03338 (2024) - [i58]Ahmad Farooq, Cristian A. Galvis-Florez, Simo Särkkä:
Quantum-Assisted Hilbert-Space Gaussian Process Regression. CoRR abs/2402.00544 (2024) - [i57]Sahel Iqbal, Adrien Corenflos, Simo Särkkä, Hany Abdulsamad:
Nesting Particle Filters for Experimental Design in Dynamical Systems. CoRR abs/2402.07868 (2024) - [i56]Adrien Corenflos, Zheng Zhao, Simo Särkkä, Jens Sjölund, Thomas B. Schön:
Conditioning diffusion models by explicit forward-backward bridging. CoRR abs/2405.13794 (2024) - [i55]Mahdi Nasiri, Sahel Iqbal, Simo Särkkä:
Physics-Informed Machine Learning for Grade Prediction in Froth Flotation. CoRR abs/2408.15267 (2024) - [i54]Sahel Iqbal, Hany Abdulsamad, Sara Pérez-Vieites, Simo Särkkä, Adrien Corenflos:
Recursive Nested Filtering for Efficient Amortized Bayesian Experimental Design. CoRR abs/2409.05354 (2024) - [i53]Casian Iacob, Hany Abdulsamad, Simo Särkkä:
A Parallel-in-Time Newton's Method for Nonlinear Model Predictive Control. CoRR abs/2409.20027 (2024) - [i52]Fatemeh Yaghoobi, Simo Särkkä:
Parallel state estimation for systems with integrated measurements. CoRR abs/2410.00627 (2024) - 2023
- [j77]Harshit Agrawal, Ari Hietanen, Simo Särkkä:
Deep Learning Based Projection Domain Metal Segmentation for Metal Artifact Reduction in Cone Beam Computed Tomography. IEEE Access 11: 100371-100382 (2023) - [j76]William J. Wilkinson, Simo Särkkä, Arno Solin:
Bayes-Newton Methods for Approximate Bayesian Inference with PSD Guarantees. J. Mach. Learn. Res. 24: 83:1-83:50 (2023) - [j75]Hao Dong, Xieyuanli Chen, Simo Särkkä, Cyrill Stachniss:
Online pole segmentation on range images for long-term LiDAR localization in urban environments. Robotics Auton. Syst. 159: 104283 (2023) - [j74]Muhammad F. Emzir, Zheng Zhao, Simo Särkkä:
Multidimensional projection filters via automatic differentiation and sparse-grid integration. Signal Process. 204: 108832 (2023) - [j73]Simo Särkkä, Ángel F. García-Fernández:
Temporal Parallelization of Dynamic Programming and Linear Quadratic Control. IEEE Trans. Autom. Control. 68(2): 851-866 (2023) - [j72]Syeda Sakira Hassan, Simo Särkkä:
Fourier-Hermite Dynamic Programming for Optimal Control. IEEE Trans. Autom. Control. 68(10): 6377-6384 (2023) - [j71]Toni Karvonen, Jon Cockayne, Filip Tronarp, Simo Särkkä:
A probabilistic Taylor expansion with Gaussian processes. Trans. Mach. Learn. Res. 2023 (2023) - [j70]Zheng Zhao, Simo Särkkä, Jens Sjölund, Thomas B. Schön:
Probabilistic Estimation of Instantaneous Frequencies of Chirp Signals. IEEE Trans. Signal Process. 71: 461-476 (2023) - [c87]Fatemeh Yaghoobi, Hany Abdulsamad, Simo Särkkä:
A Recursive Newton Method for Smoothing in Nonlinear State Space Models. EUSIPCO 2023: 1758-1762 - [c86]Simo Särkkä, Ángel F. García-Fernández:
On The Temporal Parallelisation of The Viterbi Algorithm. EUSIPCO 2023: 2018-2022 - [c85]Xiaofeng Ma, Simo Särkkä:
Indoor Positioning Methods Based on Dual Feet-Mounted IMUs With Distance Constraints. IPIN 2023: 1-6 - [c84]Arina Odnoblyudova, Caglar Hizli, St John, Andrea Cognolato, Anne Juuti, Simo Särkkä, Kirsi Pietiläinen, Pekka Marttinen:
Nonparametric modeling of the composite effect of multiple nutrients on blood glucose dynamics. ML4H@NeurIPS 2023: 428-444 - [c83]Ajinkya Gorad, Simo Särkkä:
Rao-Blackwellized Monte Carlo Data Association With Deep Metric For Object Tracking. MLSP 2023: 1-6 - [c82]Cristian A. Galvis-Florez, Daniel Reitzner, Simo Särkkä:
Single Qubit State Estimation on NISQ Devices with Limited Resources and SIC-POVMs. QCE 2023: 111-119 - [c81]Ajinkya Gorad, Sakira Hassan, Simo Särkkä:
Vessel Bearing Estimation Using Visible and Thermal Imaging. SCIA (2) 2023: 373-381 - [c80]Chetan Gupta, Rustam Latypov, Yannic Maus, Shreyas Pai, Simo Särkkä, Jan Studený, Jukka Suomela, Jara Uitto, Hossein Vahidi:
Fast Dynamic Programming in Trees in the MPC Model. SPAA 2023: 443-453 - [i51]Adrien Corenflos, Simo Särkkä:
Auxiliary MCMC and particle Gibbs samplers for parallelisable inference in latent dynamical systems. CoRR abs/2303.00301 (2023) - [i50]Chetan Gupta, Rustam Latypov, Yannic Maus, Shreyas Pai, Simo Särkkä, Jan Studený, Jukka Suomela, Jara Uitto, Hossein Vahidi:
Fast Dynamic Programming in Trees in the MPC Model. CoRR abs/2305.03693 (2023) - [i49]Fatemeh Yaghoobi, Hany Abdulsamad, Simo Särkkä:
A Recursive Newton Method for Smoothing in Nonlinear State Space Models. CoRR abs/2306.09148 (2023) - [i48]Nathanael Bosch, Adrien Corenflos, Fatemeh Yaghoobi, Filip Tronarp, Philipp Hennig, Simo Särkkä:
Parallel-in-Time Probabilistic Numerical ODE Solvers. CoRR abs/2310.01145 (2023) - [i47]Arina Odnoblyudova, Çaglar Hizli, St John, Andrea Cognolato, Anne Juuti, Simo Särkkä, Kirsi Pietiläinen, Pekka Marttinen:
Nonparametric modeling of the composite effect of multiple nutrients on blood glucose dynamics. CoRR abs/2311.03129 (2023) - [i46]Hany Abdulsamad, Sahel Iqbal, Adrien Corenflos, Simo Särkkä:
Risk-Sensitive Stochastic Optimal Control as Rao-Blackwellized Markovian Score Climbing. CoRR abs/2312.14000 (2023) - 2022
- [j69]Joel Jaskari, Jaakko Sahlsten, Theodoros Damoulas, Jeremias Knoblauch, Simo Särkkä, Leo Kärkkäinen, Kustaa Hietala, Kimmo K. Kaski:
Uncertainty-Aware Deep Learning Methods for Robust Diabetic Retinopathy Classification. IEEE Access 10: 76669-76681 (2022) - [j68]Adrien Corenflos, Nicolas Chopin, Simo Särkkä:
De-Sequentialized Monte Carlo: a parallel-in-time particle smoother. J. Mach. Learn. Res. 23: 283:1-283:39 (2022) - [j67]Zheng Zhao, Simo Särkkä:
Non-Linear Gaussian Smoothing With Taylor Moment Expansion. IEEE Signal Process. Lett. 29: 80-84 (2022) - [j66]Rui Gao, Simo Särkkä, Rubén M. Clavería, Simon J. Godsill:
Autonomous Tracking and State Estimation With Generalized Group Lasso. IEEE Trans. Cybern. 52(11): 12056-12070 (2022) - [j65]Sarang Thombre, Zheng Zhao, Henrik Ramm-Schmidt, José M. Vallet Garcia, Tuomo Malkamäki, Sergey Nikolskiy, Toni Hammarberg, Hiski Nuortie, Mohammad Zahidul H. Bhuiyan, Simo Särkkä, Ville V. Lehtola:
Sensors and AI Techniques for Situational Awareness in Autonomous Ships: A Review. IEEE Trans. Intell. Transp. Syst. 23(1): 64-83 (2022) - [j64]Simo Särkkä, Lassi Roininen, Manon Kok, Roland Hostettler, Andreas Hauptmann:
Guest Editorial: MLSP 2020 Special Issue. J. Signal Process. Syst. 94(2): 131-132 (2022) - [c79]Adrien Corenflos, Zheng Zhao, Simo Särkkä:
Temporal Gaussian Process Regression in Logarithmic Time. FUSION 2022: 1-5 - [c78]Muhammad F. Emzir, Niki A. Loppi, Zheng Zhao, Syeda Sakira Hassan, Simo Särkkä:
Fast optimize-and-sample method for differentiable Galerkin approximations of multi-layered Gaussian process priors. FUSION 2022: 1-7 - [c77]Matti Raitoharju, Roland Hostettler, Simo Särkkä:
Posterior linearisation filter for non-linear state transformation noises. FUSION 2022: 1-6 - [c76]Filip Tronarp, Simo Särkkä:
Continuous-Discrete Filtering and Smoothing on Submanifolds of Euclidean Space. FUSION 2022: 1-8 - [i45]Joel Jaskari, Jaakko Sahlsten, Theodoros Damoulas, Jeremias Knoblauch, Simo Särkkä, Leo Kärkkäinen, Kustaa Hietala, Kimmo Kaski:
Uncertainty-aware deep learning methods for robust diabetic retinopathy classification. CoRR abs/2201.09042 (2022) - [i44]Adrien Corenflos, Nicolas Chopin, Simo Särkkä:
De-Sequentialized Monte Carlo: a parallel-in-time particle smoother. CoRR abs/2202.02264 (2022) - [i43]Sakira Hassan, Simo Särkkä:
Fourier-Hermite Dynamic Programming for Optimal Control. CoRR abs/2202.13453 (2022) - [i42]Fatemeh Yaghoobi, Adrien Corenflos, Sakira Hassan, Simo Särkkä:
Parallel square-root statistical linear regression for inference in nonlinear state space models. CoRR abs/2207.00426 (2022) - [i41]Hao Dong, Xieyuanli Chen, Simo Särkkä, Cyrill Stachniss:
Online Pole Segmentation on Range Images for Long-term LiDAR Localization in Urban Environments. CoRR abs/2208.07364 (2022) - [i40]Harshit Agrawal, Ari Hietanen, Simo Särkkä:
Metal artifact correction in cone beam computed tomography using synthetic X-ray data. CoRR abs/2208.08288 (2022) - [i39]Simo Särkkä, Ángel F. García-Fernández:
Temporal Parallelisation of the HJB Equation and Continuous-Time Linear Quadratic Control. CoRR abs/2212.11744 (2022) - 2021
- [j63]Toni Karvonen, Simo Särkkä, Ken'ichiro Tanaka:
Kernel-based interpolation at approximate Fekete points. Numer. Algorithms 87(1): 445-468 (2021) - [j62]Toni Karvonen, Simo Särkkä, Ken'ichiro Tanaka:
Correction to: Kernel-based interpolation at approximate Fekete points. Numer. Algorithms 87(1): 469-471 (2021) - [j61]Filip Tronarp, Simo Särkkä, Philipp Hennig:
Bayesian ODE solvers: the maximum a posteriori estimate. Stat. Comput. 31(3): 23 (2021) - [j60]Zheng Zhao, Muhammad F. Emzir, Simo Särkkä:
Deep state-space Gaussian processes. Stat. Comput. 31(6): 75 (2021) - [j59]Simo Särkkä, Ángel F. García-Fernández:
Temporal Parallelization of Bayesian Smoothers. IEEE Trans. Autom. Control. 66(1): 299-306 (2021) - [j58]Jakub Prüher, Toni Karvonen, Chris J. Oates, Ondrej Straka, Simo Särkkä:
Improved Calibration of Numerical Integration Error in Sigma-Point Filters. IEEE Trans. Autom. Control. 66(3): 1286-1292 (2021) - [j57]Zheng Zhao, Toni Karvonen, Roland Hostettler, Simo Särkkä:
Taylor Moment Expansion for Continuous-Discrete Gaussian Filtering. IEEE Trans. Autom. Control. 66(9): 4460-4467 (2021) - [j56]Syeda Sakira Hassan, Simo Särkkä, Ángel F. García-Fernández:
Temporal Parallelization of Inference in Hidden Markov Models. IEEE Trans. Signal Process. 69: 4875-4887 (2021) - [c75]Leo McCormack, Archontis Politis, Simo Särkkä, Ville Pulkki:
Real-Time Tracking of Multiple Acoustical Sources Utilising Rao-Blackwellised Particle Filtering. EUSIPCO 2021: 206-210 - [c74]Fatemeh Yaghoobi, Adrien Corenflos, Sakira Hassan, Simo Särkkä:
Parallel Iterated Extended and Sigma-Point Kalman Smoothers. ICASSP 2021: 5350-5354 - [c73]Matti Raitoharju, Henri Nurminen, Demet Cilden-Guler, Simo Särkkä:
Kalman filtering with empirical noise models. ICL-GNSS 2021: 1-7 - [c72]Harshit Agrawal, Ari Hietanen, Simo Särkkä:
Metal Artifact Reduction In Cone-Beam Extremity Images Using Gated Convolutions. ISBI 2021: 1087-1090 - [c71]Simo Särkkä, Christos Merkatas, Toni Karvonen:
Gaussian Approximations of SDES in Metropolis-Adjusted Langevin Algorithms. MLSP 2021: 1-6 - [i38]Fatemeh Yaghoobi, Adrien Corenflos, Sakira Hassan, Simo Särkkä:
Parallel Iterated Extended and Sigma-point Kalman Smoothers. CoRR abs/2102.00514 (2021) - [i37]Toni Karvonen, Jon Cockayne, Filip Tronarp, Simo Särkkä:
A Probabilistic Taylor Expansion with Applications in Filtering and Differential Equations. CoRR abs/2102.00877 (2021) - [i36]Sakira Hassan, Simo Särkkä, Ángel F. García-Fernández:
Temporal Parallelization of Inference in Hidden Markov Models. CoRR abs/2102.05743 (2021) - [i35]Adrien Corenflos, Zheng Zhao, Simo Särkkä:
Gaussian Process Regression in Logarithmic Time. CoRR abs/2102.09964 (2021) - [i34]Simo Särkkä, Ángel F. García-Fernández:
Temporal Parallelisation of Dynamic Programming and Linear Quadratic Control. CoRR abs/2104.03186 (2021) - [i33]David Luengo, Luca Martino, Mónica F. Bugallo, Victor Elvira, Simo Särkkä:
A Survey of Monte Carlo Methods for Parameter Estimation. CoRR abs/2107.11820 (2021) - [i32]Zheng Zhao, Simo Särkkä:
Non-linear Gaussian smoothing with Taylor moment expansion. CoRR abs/2110.01396 (2021) - [i31]William J. Wilkinson, Simo Särkkä, Arno Solin:
Bayes-Newton Methods for Approximate Bayesian Inference with PSD Guarantees. CoRR abs/2111.01721 (2021) - 2020
- [j55]Joel Jaskari, Janne Myllärinen, Markus Leskinen, Ali Bahrami Rad, Jaakko Hollmén, Sture Andersson, Simo Särkkä:
Machine Learning Methods for Neonatal Mortality and Morbidity Classification. IEEE Access 8: 123347-123358 (2020) - [j54]Toni Karvonen, Simo Särkkä:
Worst-case optimal approximation with increasingly flat Gaussian kernels. Adv. Comput. Math. 46(2): 21 (2020) - [j53]David Luengo, Luca Martino, Mónica F. Bugallo, Víctor Elvira, Simo Särkkä:
A survey of Monte Carlo methods for parameter estimation. EURASIP J. Adv. Signal Process. 2020(1): 25 (2020) - [j52]Toni Karvonen, George Wynne, Filip Tronarp, Chris J. Oates, Simo Särkkä:
Maximum Likelihood Estimation and Uncertainty Quantification for Gaussian Process Approximation of Deterministic Functions. SIAM/ASA J. Uncertain. Quantification 8(3): 926-958 (2020) - [j51]Arno Solin, Simo Särkkä:
Hilbert space methods for reduced-rank Gaussian process regression. Stat. Comput. 30(2): 419-446 (2020) - [j50]Toni Karvonen, Silvère Bonnabel, Eric Moulines, Simo Särkkä:
On Stability of a Class of Filters for Nonlinear Stochastic Systems. SIAM J. Control. Optim. 58(4): 2023-2049 (2020) - [j49]Matti Raitoharju, Ángel F. García-Fernández, Roland Hostettler, Robert Piché, Simo Särkkä:
Gaussian mixture models for signal mapping and positioning. Signal Process. 168 (2020) - [j48]Roland Hostettler, Filip Tronarp, Ángel F. García-Fernández, Simo Särkkä:
Importance Densities for Particle Filtering Using Iterated Conditional Expectations. IEEE Signal Process. Lett. 27: 211-215 (2020) - [j47]Rui Gao, Filip Tronarp, Simo Särkkä:
Variable Splitting Methods for Constrained State Estimation in Partially Observed Markov Processes. IEEE Signal Process. Lett. 27: 1305-1309 (2020) - [j46]Hüseyin Yigitler, Ossi Kaltiokallio, Roland Hostettler, Alemayehu Solomon Abrar, Riku Jäntti, Neal Patwari, Simo Särkkä:
RSS Models for Respiration Rate Monitoring. IEEE Trans. Mob. Comput. 19(3): 680-696 (2020) - [j45]Zheng Zhao, Simo Särkkä, Ali Bahrami Rad:
Kalman-based Spectro-Temporal ECG Analysis using Deep Convolutional Networks for Atrial Fibrillation Detection. J. Signal Process. Syst. 92(7): 621-636 (2020) - [c70]Janne Mustaniemi, Juho Kannala, Jiri Matas, Simo Särkkä, Janne Heikkilä:
LSD_2 - Joint Denoising and Deblurring of Short and Long Exposure Images with CNNs. BMVC 2020 - [c69]Salla Aario, Ajinkya Gorad, Miika Arvonen, Simo Särkkä:
Respiratory Pattern Recognition from Low-Resolution Thermal Imaging. ESANN 2020: 469-474 - [c68]Rui Gao, Simo Särkkä:
Augmented Sigma-Point Lagrangian Splitting Method for Sparse Nonlinear State Estimation. EUSIPCO 2020: 2090-2094 - [c67]Zheng Zhao, Filip Tronarp, Roland Hostettler, Simo Särkkä:
State-Space Gaussian Process for Drift Estimation in Stochastic Differential Equations. ICASSP 2020: 5295-5299 - [c66]Simo Särkkä, Lennart Svensson:
Levenberg-Marquardt and Line-Search Extended Kalman Smoothers. ICASSP 2020: 5875-5879 - [c65]Ajinkya Gorad, Zheng Zhao, Simo Särkkä:
Parameter Estimation in Non-Linear State-Space Models by Automatic Differentiation of Non-Linear Kalman Filters. MLSP 2020: 1-6 - [i30]Toni Karvonen, George Wynne, Filip Tronarp, Chris J. Oates, Simo Särkkä:
Maximum likelihood estimation and uncertainty quantification for Gaussian process approximation of deterministic functions. CoRR abs/2001.10965 (2020) - [i29]Jarkko Suuronen, Muhammad F. Emzir, Sari Lasanen, Simo Särkkä, Lassi Roininen:
Enhancing Industrial X-ray Tomography by Data-Centric Statistical Methods. CoRR abs/2003.03814 (2020) - [i28]Filip Tronarp, Simo Särkkä, Philipp Hennig:
Bayesian ODE Solvers: The Maximum A Posteriori Estimate. CoRR abs/2004.00623 (2020) - [i27]Rui Gao, Filip Tronarp, Simo Särkkä:
Variable Splitting Methods for Constrained State Estimation in Partially Observed Markov Processes. CoRR abs/2005.08275 (2020) - [i26]Zheng Zhao, Muhammad F. Emzir, Simo Särkkä:
Deep State-Space Gaussian Processes. CoRR abs/2008.04733 (2020)
2010 – 2019
- 2019
- [j44]Juha Sarmavuori, Simo Särkkä:
Numerical integration as a finite matrix approximation to multiplication operator. J. Comput. Appl. Math. 353: 283-291 (2019) - [j43]Michael Schober, Simo Särkkä, Philipp Hennig:
A probabilistic model for the numerical solution of initial value problems. Stat. Comput. 29(1): 99-122 (2019) - [j42]Toni Karvonen, Simo Särkkä, Chris J. Oates:
Symmetry exploits for Bayesian cubature methods. Stat. Comput. 29(6): 1231-1248 (2019) - [j41]Filip Tronarp, Hans Kersting, Simo Särkkä, Philipp Hennig:
Probabilistic solutions to ordinary differential equations as nonlinear Bayesian filtering: a new perspective. Stat. Comput. 29(6): 1297-1315 (2019) - [j40]Toni Karvonen, Motonobu Kanagawa, Simo Särkkä:
On the positivity and magnitudes of Bayesian quadrature weights. Stat. Comput. 29(6): 1317-1333 (2019) - [j39]Filip Tronarp, Simo Särkkä:
Iterative statistical linear regression for Gaussian smoothing in continuous-time non-linear stochastic dynamic systems. Signal Process. 159: 1-12 (2019) - [j38]Filip Tronarp, Toni Karvonen, Simo Särkkä:
Student's $t$-Filters for Noise Scale Estimation. IEEE Signal Process. Lett. 26(2): 352-356 (2019) - [j37]Ángel F. García-Fernández, Filip Tronarp, Simo Särkkä:
Gaussian Process Classification Using Posterior Linearization. IEEE Signal Process. Lett. 26(5): 735-739 (2019) - [j36]Roland Hostettler, Simo Särkkä:
Rao-Blackwellized Gaussian Smoothing. IEEE Trans. Autom. Control. 64(1): 305-312 (2019) - [j35]Simo Särkkä, Mauricio A. Álvarez, Neil D. Lawrence:
Gaussian Process Latent Force Models for Learning and Stochastic Control of Physical Systems. IEEE Trans. Autom. Control. 64(7): 2953-2960 (2019) - [j34]Ángel F. García-Fernández, Filip Tronarp, Simo Särkkä:
Gaussian Target Tracking With Direction-of-Arrival von Mises-Fisher Measurements. IEEE Trans. Signal Process. 67(11): 2960-2972 (2019) - [j33]Rui Gao, Filip Tronarp, Simo Särkkä:
Iterated Extended Kalman Smoother-Based Variable Splitting for L1-Regularized State Estimation. IEEE Trans. Signal Process. 67(19): 5078-5092 (2019) - [j32]Ángel F. García-Fernández, Roland Hostettler, Simo Särkkä:
Rao-Blackwellized Posterior Linearization Backward SLAM. IEEE Trans. Veh. Technol. 68(5): 4734-4747 (2019) - [c64]Roland Hostettler, Ángel F. García-Fernández, Filip Tronarp, Simo Särkkä:
Joint Calibration of Inertial Sensors and Magnetometers using von Mises-Fisher Filtering and Expectation Maximization. FUSION 2019: 1-8 - [c63]Matti Raitoharju, Ángel F. García-Fernández, Simo Särkkä:
Partitioned Update Binomial Gaussian Mixture Filter. FUSION 2019: 1-8 - [c62]Filip Tronarp, Simo Särkkä:
Updates in Bayesian Filtering by Continuous Projections on a Manifold of Densities. ICASSP 2019: 5032-5036 - [c61]Muhammad F. Emzir, Sari Lasanen, Zenith Purisha, Simo Särkkä:
Hilbert-Space Reduced-Rank Methods For Deep Gaussian Processes. MLSP 2019: 1-6 - [c60]Rui Gao, Filip Tronarp, Zheng Zhao, Simo Särkkä:
Regularized State Estimation And Parameter Learning Via Augmented Lagrangian Kalman Smoother Method. MLSP 2019: 1-6 - [c59]Roland Hostettler, Simo Särkkä:
Rejection-Sampling-Based Ancestor Sampling for Particle Gibbs. MLSP 2019: 1-6 - [c58]Toni Karvonen, Filip Tronarp, Simo Särkkä:
Asymptotics of Maximum Likelihood Parameter Estimates For Gaussian Processes: The Ornstein-Uhlenbeck Prior. MLSP 2019: 1-6 - [c57]Janne Mustaniemi, Juho Kannala, Simo Särkkä, Jiri Matas, Janne Heikkilä:
Gyroscope-Aided Motion Deblurring with Deep Networks. WACV 2019: 1914-1922 - [i25]Morteza Zabihi, Ali Bahrami Rad, Serkan Kiranyaz, Simo Särkkä, Moncef Gabbouj:
1D Convolutional Neural Network Models for Sleep Arousal Detection. CoRR abs/1903.01552 (2019) - [i24]Rui Gao, Filip Tronarp, Simo Särkkä:
Iterated Extended Kalman Smoother-based Variable Splitting for L1-Regularized State Estimation. CoRR abs/1903.08605 (2019) - [i23]Simo Särkkä, Ángel F. García-Fernández:
Temporal Parallelization of Bayesian Filters and Smoothers. CoRR abs/1905.13002 (2019) - [i22]Toni Karvonen, Simo Särkkä:
Worst-case optimal approximation with increasingly flat Gaussian kernels. CoRR abs/1906.02096 (2019) - [i21]Simo Särkkä:
The Use of Gaussian Processes in System Identification. CoRR abs/1907.06066 (2019) - [i20]Ali Bahrami Rad, Morteza Zabihi, Zheng Zhao, Moncef Gabbouj, Aggelos K. Katsaggelos, Simo Särkkä:
Automated Polysomnography Analysis for Detection of Non-Apneic and Non-Hypopneic Arousals using Feature Engineering and a Bidirectional LSTM Network. CoRR abs/1909.02971 (2019) - [i19]Toni Karvonen, Simo Särkkä, Ken'ichiro Tanaka:
Kernel-based interpolation at approximate Fekete points. CoRR abs/1912.07316 (2019) - [i18]Juha Sarmavuori, Simo Särkkä:
On the Convergence of Numerical Integration as a Finite Matrix Approximation to Multiplication Operator. CoRR abs/1912.07325 (2019) - 2018
- [j31]Olli-Pekka Rinta-Koski, Simo Särkkä, Jaakko Hollmén, Markus Leskinen, Sture Andersson:
Gaussian process classification for prediction of in-hospital mortality among preterm infants. Neurocomputing 298: 134-141 (2018) - [j30]Toni Karvonen, Simo Särkkä:
Fully Symmetric Kernel Quadrature. SIAM J. Sci. Comput. 40(2) (2018) - [j29]Filip Tronarp, Ángel F. García-Fernández, Simo Särkkä:
Iterative Filtering and Smoothing in Nonlinear and Non-Gaussian Systems Using Conditional Moments. IEEE Signal Process. Lett. 25(3): 408-412 (2018) - [j28]Arno Solin, Manon Kok, Niklas Wahlstrom, Thomas B. Schön, Simo Särkkä:
Modeling and Interpolation of the Ambient Magnetic Field by Gaussian Processes. IEEE Trans. Robotics 34(4): 1112-1127 (2018) - [j27]Ángel F. García-Fernández, Lennart Svensson, Simo Särkkä:
Cooperative Localization Using Posterior Linearization Belief Propagation. IEEE Trans. Veh. Technol. 67(1): 832-836 (2018) - [c56]Toni Karvonen, Silvère Bonnabel, Eric Moulines, Simo Särkkä:
Bounds on the Covariance Matrix of a Class of Kalman-Bucy Filters for Systems with Non-Linear Dynamics. CDC 2018: 7176-7181 - [c55]Morteza Zabihi, Ali Bahrami Rad, Simo Särkkä, Serkan Kiranyaz, Aggelos K. Katsaggelos, Moncef Gabbouj:
Automatic Sleep Arousal Detection Using Multimodal Biosignal Analysis. CinC 2018: 1-4 - [c54]Filip Tronarp, Narayan Puthanmadam Subramaniyam, Simo Särkkä, Lauri Parkkonen:
Tracking of dynamic functional connectivity from MEG data with Kalman filtering. EMBC 2018: 1003-1006 - [c53]Rui Gao, Filip Tronarp, Simo Särkkä:
Combined Analysis-L1 and Total Variation ADMM with Applications to MEG Brain Imaging and Signal Reconstruction. EUSIPCO 2018: 1930-1934 - [c52]Roland Hostettler, Tuomas Lumikari, Lauri Palva, Tuomo Nieminen, Simo Särkkä:
Motion Artifact Reduction in Ambulatory Electrocardiography Using Inertial Measurement Units and Kalman Filtering. FUSION 2018: 1-8 - [c51]Filip Tronarp, Simo Särkkä:
Non-Linear Continuous-Discrete Smoothing by Basis Function Expansions of Brownian Motion. FUSION 2018: 1-8 - [c50]Filip Tronarp, Roland Hostettler, Simo Särkkä:
Continuous-Discrete von Mises-Fisher Filtering on S2 for Reference Vector Tracking. FUSION 2018: 1345-1352 - [c49]Janne Mustaniemi, Juho Kannala, Simo Särkkä, Jiri Matas, Janne Heikkilä:
Fast Motion Deblurring for Feature Detection and Matching Using Inertial Measurements. ICPR 2018: 3068-3073 - [c48]Kimmo Suotsalo, Simo Särkkä:
On-Line Bayesian parameter estimation in electrocardiogram State Space Models. MLSP 2018: 1-6 - [c47]Filip Tronarp, Toni Karvonen, Simo Särkkä:
Mixture Representation of the MatéRn class with Applications in State Space Approximations and Bayesian quadrature. MLSP 2018: 1-6 - [c46]Zheng Zhao, Simo Särkkä, Ali Bahrami Rad:
Spectro-Temporal ECG Analysis for atrial fibrillation Detection. MLSP 2018: 1-6 - [c45]Toni Karvonen, Chris J. Oates, Simo Särkkä:
A Bayes-Sard Cubature Method. NeurIPS 2018: 5886-5897 - [i17]Janne Mustaniemi, Juho Kannala, Simo Särkkä, Jiri Matas, Janne Heikkilä:
Fast Motion Deblurring for Feature Detection and Matching Using Inertial Measurements. CoRR abs/1805.08542 (2018) - [i16]Zenith Purisha, Carl Jidling, Niklas Wahlström, Simo Särkkä, Thomas B. Schön:
Probabilistic approach to limited-data computed tomography reconstruction. CoRR abs/1809.03779 (2018) - [i15]Ángel F. García-Fernández, Filip Tronarp, Simo Särkkä:
Gaussian process classification using posterior linearisation. CoRR abs/1809.04967 (2018) - [i14]Janne Mustaniemi, Juho Kannala, Simo Särkkä, Jiri Matas, Janne Heikkilä:
Inertial-aided Motion Deblurring with Deep Networks. CoRR abs/1810.00986 (2018) - [i13]Janne Mustaniemi, Juho Kannala, Jiri Matas, Simo Särkkä, Janne Heikkilä:
LSD2 - Joint Denoising and Deblurring of Short and Long Exposure Images with Convolutional Neural Networks. CoRR abs/1811.09485 (2018) - [i12]Jakub Prüher, Toni Karvonen, Chris J. Oates, Ondrej Straka, Simo Särkkä:
Improved Calibration of Numerical Integration Error in Sigma-Point Filters. CoRR abs/1811.11474 (2018) - [i11]Zheng Zhao, Simo Särkkä, Ali Bahrami Rad:
Kalman-based Spectro-Temporal ECG Analysis using Deep Convolutional Networks for Atrial Fibrillation Detection. CoRR abs/1812.05555 (2018) - [i10]Toni Karvonen, Motonobu Kanagawa, Simo Särkkä:
On the positivity and magnitudes of Bayesian quadrature weights. CoRR abs/1812.08509 (2018) - 2017
- [j26]Patrick R. Conrad, Mark A. Girolami, Simo Särkkä, Andrew M. Stuart, Konstantinos Zygalakis:
Statistical analysis of differential equations: introducing probability measures on numerical solutions. Stat. Comput. 27(4): 1065-1082 (2017) - [j25]Ángel F. García-Fernández, Lennart Svensson, Simo Särkkä:
Iterated Posterior Linearization Smoother. IEEE Trans. Autom. Control. 62(4): 2056-2063 (2017) - [c44]Olli-Pekka Rinta-Koski, Simo Särkkä, Jaakko Hollmén, Markus Leskinen, Sture Andersson:
Prediction of preterm infant mortality with Gaussian process classification. ESANN 2017 - [c43]Roland Hostettler, Ossi Kaltiokallio, Hüseyin Yigitler, Simo Särkkä, Riku Jäntti:
RSS-based respiratory rate monitoring using periodic Gaussian processes and Kalman filtering. EUSIPCO 2017: 256-260 - [c42]Jakub Prüher, Filip Tronarp, Toni Karvonen, Simo Särkkä, Ondrej Straka:
Student-t process quadratures for filtering of non-linear systems with heavy-tailed noise. FUSION 2017: 1-8 - [c41]Janne Mustaniemi, Juho Kannala, Simo Särkkä, Jiri Matas, Janne Heikkilä:
Inertial-based scale estimation for structure from motion on mobile devices. IROS 2017: 4394-4401 - [c40]Alexander Grigorievskiy, Neil D. Lawrence, Simo Särkkä:
Parallelizable sparse inverse formulation Gaussian processes (SpInGP). MLSP 2017: 1-6 - [c39]Roland Hostettler, Simo Särkkä, Simon J. Godsill:
Rao-Blackwellized particle mcmc for parameter estimation in spatio-temporal Gaussian processes. MLSP 2017: 1-6 - [c38]Toni Karvonen, Simo Särkkä:
Classical quadrature rules via Gaussian processes. MLSP 2017: 1-6 - [c37]Kimmo Suotsalo, Simo Särkkä:
A linear stochastic state space model for electrocardiograms. MLSP 2017: 1-6 - [c36]Kimmo Suotsalo, Simo Särkkä:
Detecting malignant ventricular arrhythmias in electrocardiograms by Gaussian process classification. MLSP 2017: 1-5 - [i9]Toni Karvonen, Simo Särkkä:
Fully symmetric kernel quadrature. CoRR abs/1703.06359 (2017) - [i8]Simo Särkkä, Mauricio A. Álvarez, Neil D. Lawrence:
Gaussian Process Latent Force Models for Learning and Stochastic Control of Physical Systems. CoRR abs/1709.05409 (2017) - 2016
- [j24]Fredrik Lindsten, Pete Bunch, Simo Särkkä, Thomas B. Schön, Simon J. Godsill:
Rao-Blackwellized Particle Smoothers for Conditionally Linear Gaussian Models. IEEE J. Sel. Top. Signal Process. 10(2): 353-365 (2016) - [j23]Isambi S. Mbalawata, Simo Särkkä:
Moment conditions for convergence of particle filters with unbounded importance weights. Signal Process. 118: 133-138 (2016) - [c35]Andreas Svensson, Arno Solin, Simo Särkkä, Thomas B. Schön:
Computationally Efficient Bayesian Learning of Gaussian Process State Space Models. AISTATS 2016: 213-221 - [c34]Toni Karvonen, Simo Särkkä:
Fourier-Hermite series for stochastic stability analysis of non-linear Kalman filters. FUSION 2016: 1829-1836 - [c33]Filip Tronarp, Roland Hostettler, Simo Särkkä:
Sigma-point filtering for nonlinear systems with non-additive heavy-tailed noise. FUSION 2016: 1859-1866 - [c32]Simo Särkkä, Eric Moulines:
On the LP-convergence of a Girsanov theorem based particle filter. ICASSP 2016: 3989-3993 - [c31]Roland Hostettler, Simo Särkkä:
IMU and magnetometer modeling for smartphone-based PDR. IPIN 2016: 1-8 - [c30]Toni Karvonen, Simo Särkkä:
Approximate state-space Gaussian processes via spectral transformation. MLSP 2016: 1-6 - [c29]Jakub Prüher, Simo Särkkä:
On the use of gradient information in Gaussian process quadratures. MLSP 2016: 1-6 - [i7]Michael Schober, Simo Särkkä, Philipp Hennig:
A probabilistic model for the numerical solution of initial value problems. CoRR abs/1610.05261 (2016) - [i6]Janne Mustaniemi, Juho Kannala, Simo Särkkä, Jiri Matas, Janne Heikkilä:
Inertial-Based Scale Estimation for Structure from Motion on Mobile Devices. CoRR abs/1611.09498 (2016) - 2015
- [j22]Sean Anderson, Timothy D. Barfoot, Chi Hay Tong, Simo Särkkä:
Batch nonlinear continuous-time trajectory estimation as exactly sparse Gaussian process regression. Auton. Robots 39(3): 221-238 (2015) - [j21]Isambi S. Mbalawata, Simo Särkkä, Matti Vihola, Heikki Haario:
Adaptive Metropolis algorithm using variational Bayesian adaptive Kalman filter. Comput. Stat. Data Anal. 83: 101-115 (2015) - [j20]Juho Kokkala, Simo Särkkä:
Combining particle MCMC with Rao-Blackwellized Monte Carlo data association for parameter estimation in multiple target tracking. Digit. Signal Process. 47: 84-95 (2015) - [j19]Xi Chen, Simo Särkkä, Simon J. Godsill:
A Bayesian particle filtering method for brain source localisation. Digit. Signal Process. 47: 192-204 (2015) - [j18]Simo Särkkä, Jouni Hartikainen, Isambi Sailon Mbalawata, Heikki Haario:
Posterior inference on parameters of stochastic differential equations via non-linear Gaussian filtering and adaptive MCMC. Stat. Comput. 25(2): 427-437 (2015) - [j17]Juha Ala-Luhtala, Simo Särkkä, Robert Piché:
Gaussian filtering and variational approximations for Bayesian smoothing in continuous-discrete stochastic dynamic systems. Signal Process. 111: 124-136 (2015) - [j16]Ángel F. García-Fernández, Lennart Svensson, Mark R. Morelande, Simo Särkkä:
Posterior Linearization Filter: Principles and Implementation Using Sigma Points. IEEE Trans. Signal Process. 63(20): 5561-5573 (2015) - [c28]Arno Solin, Simo Särkkä:
State Space Methods for Efficient Inference in Student-t Process Regression. AISTATS 2015 - [c27]Andreas Svensson, Thomas B. Schön, Arno Solin, Simo Särkkä:
Nonlinear state space model identification using a regularized basis function expansion. CAMSAP 2015: 481-484 - [c26]Juho Kokkala, Simo Särkkä:
Split-Gaussian particle filter. EUSIPCO 2015: 484-488 - [c25]Jayaprasad Bojja, Jussi Collin, Simo Särkkä, Jarmo Takala:
Pedestrian localization in moving platforms using dead reckoning, particle filtering and map matching. ICASSP 2015: 1116-1120 - [c24]Simo Särkkä, Ville Tolvanen, Juho Kannala, Esa Rahtu:
Adaptive Kalman filtering and smoothing for gravitation tracking in mobile systems. IPIN 2015: 1-7 - [i5]Arno Solin, Manon Kok, Niklas Wahlström, Thomas B. Schön, Simo Särkkä:
Modeling and interpolation of the ambient magnetic field by Gaussian processes. CoRR abs/1509.04634 (2015) - [i4]Andreas Svensson, Thomas B. Schön, Arno Solin, Simo Särkkä:
Nonlinear State Space Model Identification Using a Regularized Basis Function Expansion. CoRR abs/1510.00563 (2015) - 2014
- [j15]Simon M. J. Lyons, Simo Särkkä, Amos J. Storkey:
Series Expansion Approximations of Brownian Motion for Non-Linear Kalman Filtering of Diffusion Processes. IEEE Trans. Signal Process. 62(6): 1514-1524 (2014) - [c23]Arno Solin, Simo Särkkä:
Explicit Link Between Periodic Covariance Functions and State Space Models. AISTATS 2014: 904-912 - [c22]Isambi S. Mbalawata, Simo Särkkä:
Weight moment conditions for L4 convergence of particle filters for unbounded test functions. EUSIPCO 2014: 1207-1211 - [c21]Simo Särkkä, Ville Viikari, Kaarle Jaakkola:
RFID-based butterfly location sensing system. EUSIPCO 2014: 2045-2049 - [c20]Juho Kokkala, Arno Solin, Simo Särkkä:
Expectation maximization based parameter estimation by sigma-point and particle smoothing. FUSION 2014: 1-8 - [c19]Simo Särkkä, Jouni Hartikainen, Lennart Svensson, Fredrik Sandblom:
Gaussian process quadratures in nonlinear sigma-point filtering and smoothing. FUSION 2014: 1-8 - [c18]Isambi S. Mbalawata, Simo Särkkä:
On the L4 convergence of particle filters with general importance distributions. ICASSP 2014: 8048-8052 - [c17]Simo Särkkä, Robert Piché:
On convergence and accuracy of state-space approximations of squared exponential covariance functions. MLSP 2014: 1-6 - [c16]Arno Solin, Simo Särkkä:
Gaussian quadratures for state space approximation of scale mixtures of squared exponential covariance functions. MLSP 2014: 1-6 - [c15]Arno Solin, Simo Särkkä:
The 10th annual MLSP competition: First place. MLSP 2014: 1-3 - [c14]Tim D. Barfoot, Chi Hay Tong, Simo Särkkä:
Batch Continuous-Time Trajectory Estimation as Exactly Sparse Gaussian Process Regression. Robotics: Science and Systems 2014 - [i3]Sean Anderson, Timothy D. Barfoot, Chi Hay Tong, Simo Särkkä:
Batch Nonlinear Continuous-Time Trajectory Estimation as Exactly Sparse Gaussian Process Regression. CoRR abs/1412.0630 (2014) - 2013
- [b1]Simo Särkkä:
Bayesian Filtering and Smoothing. Institute of Mathematical Statistics textbooks 3, Cambridge University Press 2013, ISBN 978-1-10-761928-9, pp. I-XXII, 1-232 - [j14]Isambi S. Mbalawata, Simo Särkkä, Heikki Haario:
Parameter estimation in stochastic differential equations with Markov chain Monte Carlo and non-linear Kalman filtering. Comput. Stat. 28(3): 1195-1223 (2013) - [j13]Simo Särkkä, Juha Sarmavuori:
Gaussian filtering and smoothing for continuous-discrete dynamic systems. Signal Process. 93(2): 500-510 (2013) - [j12]Simo Särkkä, Arno Solin, Jouni Hartikainen:
Spatiotemporal Learning via Infinite-Dimensional Bayesian Filtering and Smoothing: A Look at Gaussian Process Regression Through Kalman Filtering. IEEE Signal Process. Mag. 30(4): 51-61 (2013) - [c13]Xi Chen, Simo Särkkä, Simon J. Godsill:
Probabilistic initiation and termination for MEG multiple dipole localization using sequential Monte Carlo methods. FUSION 2013: 580-587 - [c12]Simo Särkkä, Jouni Hartikainen:
Non-linear noise adaptive Kalman filtering via variational Bayes. MLSP 2013: 1-6 - [c11]Simo Särkkä, Arno Solin:
Continuous-Space Gaussian Process Regression and Generalized Wiener Filtering with Application to Learning Curves. SCIA 2013: 172-181 - 2012
- [j11]Simo Särkkä, Arno Solin, Aapo Nummenmaa, Aki Vehtari, Toni Auranen, Simo Vanni, Fa-Hsuan Lin:
Dynamic retrospective filtering of physiological noise in BOLD fMRI: DRIFTER. NeuroImage 60(2): 1517-1527 (2012) - [j10]Juha Sarmavuori, Simo Särkkä:
Fourier-Hermite Kalman Filter. IEEE Trans. Autom. Control. 57(6): 1511-1515 (2012) - [c10]Juha Sarmavuori, Simo Särkkä:
Fourier-Hermite Rauch-Tung-Striebel smoother. EUSIPCO 2012: 2109-2113 - [c9]Jouni Hartikainen, Mari Seppänen, Simo Särkkä:
State-Space Inference for Non-Linear Latent Force Models with Application to Satellite Orbit Prediction. ICML 2012 - [c8]Robert Piché, Simo Särkkä, Jouni Hartikainen:
Recursive outlier-robust filtering and smoothing for nonlinear systems using the multivariate student-t distribution. MLSP 2012: 1-6 - [c7]Simon M. J. Lyons, Amos J. Storkey, Simo Särkkä:
The Coloured Noise Expansion and Parameter Estimation of Diffusion Processes. NIPS 2012: 1961-1969 - [c6]Simo Särkkä, Jouni Hartikainen:
Infinite-Dimensional Kalman Filtering Approach to Spatio-Temporal Gaussian Process Regression. AISTATS 2012: 993-1001 - [i2]Jouni Hartikainen, Simo Särkkä:
Sequential Inference for Latent Force Models. CoRR abs/1202.3730 (2012) - [i1]Jouni Hartikainen, Mari Seppänen, Simo Särkkä:
State-Space Inference for Non-Linear Latent Force Models with Application to Satellite Orbit Prediction. CoRR abs/1206.4670 (2012) - 2011
- [j9]Simo Särkkä, Jouni Hartikainen:
Correction to "On Gaussian Optimal Smoothing of Nonlinear State Space Models" [Aug 10 1938-1941]. IEEE Trans. Autom. Control. 56(7): 1746 (2011) - [j8]Simo Särkkä, Antti Huovilainen:
Accurate Discretization of Analog Audio Filters With Application to Parametric Equalizer Design. IEEE ACM Trans. Audio Speech Lang. Process. 19(8): 2486-2493 (2011) - [c5]Simo Särkkä:
Linear Operators and Stochastic Partial Differential Equations in Gaussian Process Regression. ICANN (2) 2011: 151-158 - [c4]Jouni Hartikainen, Jaakko Riihimäki, Simo Särkkä:
Sparse Spatio-temporal Gaussian Processes with General Likelihoods. ICANN (1) 2011: 193-200 - [c3]Simo Särkkä:
Learning Curves for Gaussian Processes via Numerical Cubature Integration. ICANN (1) 2011: 201-208 - [c2]Jouni Hartikainen, Simo Särkkä:
Sequential Inference for Latent Force Models. UAI 2011: 311-318 - 2010
- [j7]Simo Särkkä:
Continuous-time and continuous-discrete-time unscented Rauch-Tung-Striebel smoothers. Signal Process. 90(1): 225-235 (2010) - [j6]Simo Särkkä, Jouni Hartikainen:
On Gaussian Optimal Smoothing of Non-Linear State Space Models. IEEE Trans. Autom. Control. 55(8): 1938-1941 (2010)
2000 – 2009
- 2009
- [j5]Simo Särkkä, Aapo Nummenmaa:
Recursive Noise Adaptive Kalman Filtering by Variational Bayesian Approximations. IEEE Trans. Autom. Control. 54(3): 596-600 (2009) - 2008
- [j4]Simo Särkkä:
Unscented Rauch-Tung-Striebel Smoother. IEEE Trans. Autom. Control. 53(3): 845-849 (2008) - 2007
- [j3]Simo Särkkä, Aki Vehtari, Jouko Lampinen:
CATS benchmark time series prediction by Kalman smoother with cross-validated noise density. Neurocomputing 70(13-15): 2331-2341 (2007) - [j2]Simo Särkkä, Aki Vehtari, Jouko Lampinen:
Rao-Blackwellized particle filter for multiple target tracking. Inf. Fusion 8(1): 2-15 (2007) - [j1]Simo Särkkä:
On Unscented Kalman Filtering for State Estimation of Continuous-Time Nonlinear Systems. IEEE Trans. Autom. Control. 52(9): 1631-1641 (2007) - 2000
- [c1]Aki Vehtari, Simo Särkkä, Jouko Lampinen:
On MCMC Sampling in Bayesian MLP Neural Networks. IJCNN (1) 2000: 317-322
Coauthor Index
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