Simo Särkkä

发表

Rao-Blackwellized Posterior Linearization Backward SLAM

Ángel F. García-Fernández, Roland Hostettler, Simo Särkkä, 2019, IEEE Transactions on Vehicular Technology.

A survey of Monte Carlo methods for parameter estimation

Mónica F. Bugallo, Luca Martino, David Luengo, 2020, EURASIP J. Adv. Signal Process..

Continuous-time and continuous-discrete-time unscented Rauch-Tung-Striebel smoothers

Simo Särkkä, S. Särkkä, 2010, Signal Process..

Improved Calibration of Numerical Integration Error in Sigma-Point Filters pdf

Ondrej Straka, Simo Särkkä, Jakub Prüher, 2018, IEEE Transactions on Automatic Control.

Expectation maximization based parameter estimation by sigma-point and particle smoothing

Arno Solin, Simo Särkkä, Juho Kokkala, 2014, 17th International Conference on Information Fusion (FUSION).

Probabilistic approach to limited-data computed tomography reconstruction pdf

Thomas B. Schön, Simo Särkkä, Zenith Purisha, 2018, Inverse Problems.

Bayesian Filtering and Smoothing

Simo Särkkä, S. Särkkä, Simo Srkk, 2013, Institute of Mathematical Statistics textbooks.

Gaussian filtering and smoothing for continuous-discrete dynamic systems

Simo Särkkä, Juha Sarmavuori, S. Särkkä, 2013, Signal Process..

Computationally Efficient Bayesian Learning of Gaussian Process State Space Models

Thomas B. Schön, Arno Solin, Simo Särkkä, 2015, AISTATS.

Continuous-Discrete von Mises-Fisher Filtering on S2 for Reference Vector Tracking

Roland Hostettler, Simo Särkkä, Filip Tronarp, 2018, 2018 21st International Conference on Information Fusion (FUSION).

Posterior inference on parameters of stochastic differential equations via non-linear Gaussian filtering and adaptive MCMC

Simo Särkkä, Heikki Haario, Jouni Hartikainen, 2015, Stat. Comput..

1D Convolutional Neural Network Models for Sleep Arousal Detection pdf

Moncef Gabbouj, Serkan Kiranyaz, Ali Bahrami Rad, 2019, ArXiv.

Gaussian filtering and variational approximations for Bayesian smoothing in continuous-discrete stochastic dynamic systems

Simo Särkkä, Robert Piché, Juha Ala-Luhtala, 2014, Signal Process..

Statistical analysis of differential equations: introducing probability measures on numerical solutions

Andrew M. Stuart, Simo Särkkä, Mark A. Girolami, 2016, Statistics and Computing.

Cooperative Localization Using Posterior Linearization Belief Propagation

Ángel F. García-Fernández, Simo Särkkä, Lennart Svensson, 2018, IEEE Transactions on Vehicular Technology.

Bayesian Filtering and Smoothing: What are Bayesian filtering and smoothing?

Simo Särkkä, 2013 .

Unscented Rauch-Tung-Striebel Smoother

Simo Särkkä, S. Särkkä, 2008, IEEE Trans. Autom. Control..

Rejection-Sampling-Based Ancestor Sampling for Particle Gibbs

Roland Hostettler, Simo Särkkä, R. Hostettler, 2019, 2019 IEEE 29th International Workshop on Machine Learning for Signal Processing (MLSP).

State Space Methods for Efficient Inference in Student-t Process Regression

Arno Solin, Simo Särkkä, S. Särkkä, 2015, AISTATS.

Gyroscope-Aided Motion Deblurring with Deep Networks

Jiri Matas, Juho Kannala, Simo Särkkä, 2018, 2019 IEEE Winter Conference on Applications of Computer Vision (WACV).

Maximum likelihood estimation and uncertainty quantification for Gaussian process approximation of deterministic functions pdf

Simo Särkkä, George Wynne, Toni Karvonen, 2020, SIAM/ASA J. Uncertain. Quantification.

Iterated Extended Kalman Smoother-Based Variable Splitting for $L_1$-Regularized State Estimation

Simo Särkkä, Rui Gao, Filip Tronarp, 2019, IEEE Transactions on Signal Processing.

Gaussian process quadratures in nonlinear sigma-point filtering and smoothing

Simo Särkkä, Lennart Svensson, Jouni Hartikainen, 2014, 17th International Conference on Information Fusion (FUSION).

Regularized State Estimation And Parameter Learning Via Augmented Lagrangian Kalman Smoother Method

Simo Särkkä, Zheng Zhao, Rui Gao, 2019, 2019 IEEE 29th International Workshop on Machine Learning for Signal Processing (MLSP).

The Coloured Noise Expansion and Parameter Estimation of Diffusion Processes

Simo Särkkä, Amos J. Storkey, Simon M. J. Lyons, 2012, NIPS.

Importance Densities for Particle Filtering Using Iterated Conditional Expectations

Ángel F. García-Fernández, Roland Hostettler, Simo Särkkä, 2020, IEEE Signal Processing Letters.

Adaptive Kalman filtering and smoothing for gravitation tracking in mobile systems

Esa Rahtu, Juho Kannala, Simo Särkkä, 2015, 2015 International Conference on Indoor Positioning and Indoor Navigation (IPIN).

State-Space Inference for Non-Linear Latent Force Models with Application to Satellite Orbit Prediction pdf

Simo Särkkä, Jouni Hartikainen, Mari Seppänen, 2012, ICML.

Sparse Spatio-temporal Gaussian Processes with General Likelihoods

Simo Särkkä, Jouni Hartikainen, Jaakko Riihimäki, 2011, ICANN.

Non-linear noise adaptive Kalman filtering via variational Bayes

Simo Särkkä, Jouni Hartikainen, S. Särkkä, 2013, 2013 IEEE International Workshop on Machine Learning for Signal Processing (MLSP).

Iterative statistical linear regression for Gaussian smoothing in continuous-time non-linear stochastic dynamic systems

Simo Särkkä, Filip Tronarp, S. Särkkä, 2018, Signal Process..

ON-LINE BAYESIAN PARAMETER ESTIMATION IN ELECTROCARDIOGRAM STATE SPACE MODELS

Simo Särkkä, Kimmo Suotsalo, 2018, 2018 IEEE 28th International Workshop on Machine Learning for Signal Processing (MLSP).

On the convergence of numerical integration as a finite matrix approximation to multiplication operator

Simo Särkkä, Juha Sarmavuori, S. Särkkä, 2017, Calcolo.

Probabilistic initiation and termination for MEG multiple dipole localization using sequential Monte Carlo methods

Xi Chen, Simon J. Godsill, Simo Särkkä, 2013, Proceedings of the 16th International Conference on Information Fusion.

Asymptotics of Maximum Likelihood Parameter Estimates For Gaussian Processes: The Ornstein–Uhlenbeck Prior

Simo Särkkä, Toni Karvonen, Filip Tronarp, 2019, 2019 IEEE 29th International Workshop on Machine Learning for Signal Processing (MLSP).

Rao-Blackwellized particle filter for multiple target tracking

Jouko Lampinen, Aki Vehtari, Simo Särkkä, 2007, Inf. Fusion.

Gaussian mixture models for signal mapping and positioning

Ángel F. García-Fernández, Roland Hostettler, Simo Särkkä, 2020, Signal Process..

Automated Polysomnography Analysis for Detection of Non-Apneic and Non-Hypopneic Arousals using Feature Engineering and a Bidirectional LSTM Network pdf

Aggelos K. Katsaggelos, Moncef Gabbouj, Ali Bahrami Rad, 2019, ArXiv.

The Use of Gaussian Processes in System Identification pdf

Simo Särkkä, 2019, ArXiv.

Non-Linear Continuous-Discrete Smoothing by Basis Function Expansions of Brownian Motion

Simo Särkkä, Filip Tronarp, Filip Tronarp, 2018, 2018 21st International Conference on Information Fusion (FUSION).

Time series prediction by Kalman smoother with cross-validated noise density

Jouko Lampinen, Aki Vehtari, Simo Särkkä, 2004, 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541).

Inertial-based scale estimation for structure from motion on mobile devices

Jiri Matas, Juho Kannala, Simo Särkkä, 2017, 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

On convergence and accuracy of state-space approximations of squared exponential covariance functions

Simo Särkkä, Robert Piché, S. Särkkä, 2014, 2014 IEEE International Workshop on Machine Learning for Signal Processing (MLSP).

Fourier-Hermite Kalman Filter

Simo Särkkä, Juha Sarmavuori, S. Särkkä, 2012, IEEE Transactions on Automatic Control.

Millimeter-Wave Imaging Method based on Frequency-Diverse Subarrays

Ville Viikari, Simo Särkkä, Juha Ala-Laurinaho, 2019, 2019 12th Global Symposium on Millimeter Waves (GSMM).

IMU and magnetometer modeling for smartphone-based PDR

Roland Hostettler, Simo Särkkä, S. Särkkä, 2016, 2016 International Conference on Indoor Positioning and Indoor Navigation (IPIN).

Prediction of preterm infant mortality with Gaussian process classification

Simo Särkkä, Markus Leskinen, Jaakko Hollmén, 2017, ESANN.

Gaussian process classification for prediction of in-hospital mortality among preterm infants

Simo Särkkä, Markus Leskinen, Jaakko Hollmén, 2018, Neurocomputing.

Kalman-based Spectro-Temporal ECG Analysis using Deep Convolutional Networks for Atrial Fibrillation Detection

Ali Bahrami Rad, Simo Särkkä, Zheng Zhao, 2018, Journal of Signal Processing Systems.

Detecting malignant ventricular arrhythmias in electrocardiograms by Gaussian process classification

Simo Särkkä, Kimmo Suotsalo, S. Särkkä, 2017, 2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP).

Learning Curves for Gaussian Processes via Numerical Cubature Integration

Simo Särkkä, 2011, ICANN.

LSD2 - Joint Denoising and Deblurring of Short and Long Exposure Images with Convolutional Neural Networks pdf

Jiri Matas, Juho Kannala, Simo Särkkä, 2018, ArXiv.

RSS-based respiratory rate monitoring using periodic Gaussian processes and Kalman filtering

Roland Hostettler, Simo Särkkä, Ossi Kaltiokallio, 2017, 2017 25th European Signal Processing Conference (EUSIPCO).

Approximate state-space Gaussian processes via spectral transformation

Simo Särkkä, Toni Karvonen, 2016, 2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP).

Modeling and Interpolation of the Ambient Magnetic Field by Gaussian Processes

Thomas B. Schön, Simo Särkkä, Arno Solin, 2015, IEEE Transactions on Robotics.

On the positivity and magnitudes of Bayesian quadrature weights

Simo Särkkä, Toni Karvonen, Motonobu Kanagawa, 2018, Statistics and Computing.

Advances in Neural Information Processing Systems 25 (NIPS 2012)

Simo Särkkä, Amos Storkey, Simon M. J. Lyons, 2002 .

Deep State-Space Gaussian Processes pdf

Simo Särkkä, Muhammad F. Emzir, Zheng Zhao, 2020, Stat. Comput..

Gaussian Process Classification Using Posterior Linearization

Ángel F. García-Fernández, Simo Särkkä, Filip Tronarp, 2018, IEEE Signal Processing Letters.

Recursive outlier-robust filtering and smoothing for nonlinear systems using the multivariate student-t distribution

Simo Särkkä, Robert Piché, Jouni Hartikainen, 2012, 2012 IEEE International Workshop on Machine Learning for Signal Processing.

Parameter Estimation in Non-Linear State-Space Models by Automatic Differentiation of Non-Linear Kalman Filters

Simo Särkkä, Zheng Zhao, Ajinkya Gorad, 2020, 2020 IEEE 30th International Workshop on Machine Learning for Signal Processing (MLSP).

Iterative Filtering and Smoothing in Nonlinear and Non-Gaussian Systems Using Conditional Moments

Ángel F. García-Fernández, Simo Särkkä, Filip Tronarp, 2018, IEEE Signal Processing Letters.

Fully symmetric kernel quadrature pdf

Simo Särkkä, Toni Karvonen, S. Särkkä, 2017, SIAM J. Sci. Comput..

Joint Calibration of Inertial Sensors and Magnetometers using von Mises-Fisher Filtering and Expectation Maximization

Roland Hostettler, Simo Särkkä, Filip Tronarp, 2019, 2019 22th International Conference on Information Fusion (FUSION).

Temporal Parallelization of Bayesian Smoothers

Simo Särkkä, Angel F. Garcia-Fernandez, 2021, IEEE Transactions on Automatic Control.

Moment conditions for convergence of particle filters with unbounded importance weights

Simo Särkkä, Isambi S. Mbalawata, S. Särkkä, 2014, Signal Process..

Temporal Parallelization of Bayesian Filters and Smoothers pdf

Simo Särkkä, Ángel F. Garcı́a-Fernández, S. Särkkä, 2019, ArXiv.

Bayesian Filtering and Smoothing: References

Simo Särkkä, 2013 .

Explicit Link Between Periodic Covariance Functions and State Space Models

Arno Solin, Simo Särkkä, S. Särkkä, 2014, AISTATS.

Parallelizable sparse inverse formulation Gaussian processes (SpInGP)

Neil D. Lawrence, Simo Särkkä, Alexander Grigorievskiy, 2017, 2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP).

Classical quadrature rules via Gaussian processes

Simo Särkkä, Toni Karvonen, S. Särkkä, 2017, 2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP).

Optimal Filtering with Kalman Filters and Smoothers

Arno Solin, Simo Särkkä, Jouni Hartikainen, 2011 .

Fourier-Hermite series for stochastic stability analysis of non-linear Kalman filters

Simo Särkkä, Toni Karvonen, 2016, 2016 19th International Conference on Information Fusion (FUSION).

Split-Gaussian particle filter

Simo Särkkä, Juho Kokkala, 2015, 2015 23rd European Signal Processing Conference (EUSIPCO).

RSS Models for Respiration Rate Monitoring

Riku Jäntti, Roland Hostettler, Simo Särkkä, 2017, IEEE Transactions on Mobile Computing.

Linear Operators and Stochastic Partial Differential Equations in Gaussian Process Regression

Simo Särkkä, S. Särkkä, 2011, ICANN.

Combined Analysis-L1 and Total Variation ADMM with Applications to MEG Brain Imaging and Signal Reconstruction

Simo Särkkä, Rui Gao, Filip Tronarp, 2018, 2018 26th European Signal Processing Conference (EUSIPCO).

SPECTRO-TEMPORAL ECG ANALYSIS FOR ATRIAL FIBRILLATION DETECTION

Ali Bahrami Rad, Simo Särkkä, Zheng Zhao, 2018, 2018 IEEE 28th International Workshop on Machine Learning for Signal Processing (MLSP).

Nonlinear state space model identification using a regularized basis function expansion

Thomas B. Schön, Arno Solin, Simo Särkkä, 2015, 2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP).

Variable Splitting Methods for Constrained State Estimation in Partially Observed Markov Processes

Simo Särkkä, Rui Gao, Filip Tronarp, 2020, IEEE Signal Processing Letters.

Gaussian Target Tracking With Direction-of-Arrival von Mises–Fisher Measurements

Ángel F. García-Fernández, Simo Särkkä, Filip Tronarp, 2019, IEEE Transactions on Signal Processing.

State-Space Gaussian Process for Drift Estimation in Stochastic Differential Equations

Roland Hostettler, Simo Särkkä, Zheng Zhao, 2020, ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

Infinite-Dimensional Kalman Filtering Approach to Spatio-Temporal Gaussian Process Regression

Simo Särkkä, Jouni Hartikainen, S. Särkkä, 2012, AISTATS.

Augmented Sigma-Point Lagrangian Splitting Method for Sparse Nonlinear State Estimation

Simo Särkkä, Rui Gao, S. Särkkä, 2021, 2020 28th European Signal Processing Conference (EUSIPCO).

Sequential Inference for Latent Force Models pdf

Simo Särkkä, Jouni Hartikainen, S. Särkkä, 2011, UAI.

Student-t process quadratures for filtering of non-linear systems with heavy-tailed noise

Ondrej Straka, Simo Särkkä, Jakub Prüher, 2017, 2017 20th International Conference on Information Fusion (Fusion).

Dynamic retrospective filtering of physiological noise in BOLD fMRI: DRIFTER

Aki Vehtari, Arno Solin, Simo Särkkä, 2012, NeuroImage.

Probabilistic solutions to ordinary differential equations as nonlinear Bayesian filtering: a new perspective

Simo Särkkä, Philipp Hennig, Filip Tronarp, 2018, Statistics and Computing.

Iterated Posterior Linearization Smoother

Ángel F. García-Fernández, Simo Särkkä, Lennart Svensson, 2017, IEEE Transactions on Automatic Control.

Bounds on the Covariance Matrix of a Class of Kalman-Bucy Filters for Systems with Non-Linear Dynamics

Eric Moulines, Simo Särkkä, Silvere Bonnabel, 2018, 2018 IEEE Conference on Decision and Control (CDC).

Gaussian quadratures for state space approximation of scale mixtures of squared exponential covariance functions

Arno Solin, Simo Särkkä, 2014, 2014 IEEE International Workshop on Machine Learning for Signal Processing (MLSP).

Inertial-aided Motion Deblurring with Deep Networks pdf

Jiri Matas, Juho Kannala, Simo Särkkä, 2018, ArXiv.

Rao–Blackwellized Gaussian Smoothing

Roland Hostettler, Simo Särkkä, S. Särkkä, 2019, IEEE Transactions on Automatic Control.

Tracking of dynamic functional connectivity from MEG data with Kalman filtering

Simo Särkkä, Lauri Parkkonen, Filip Tronarp, 2018, 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

On Stability of a Class of Filters for Nonlinear Stochastic Systems

Simo Särkkä, Toni Karvonen, Eric Moulines, 2018, SIAM J. Control. Optim..

Batch Continuous-Time Trajectory Estimation as Exactly Sparse Gaussian Process Regression

Simo Särkkä, Tim D. Barfoot, Chi Hay Tong, 2014, Robotics: Science and Systems.

A Bayesian particle filtering method for brain source localisation

Xi Chen, Simon J. Godsill, Simo Särkkä, 2015, Digit. Signal Process..

A Bayes-Sard Cubature Method

Simo Särkkä, Toni Karvonen, Chris J. Oates, 2018, NeurIPS.

Correction to "On Gaussian Optimal Smoothing of Nonlinear State Space Models" [Aug 10 1938-1941]

Simo Särkkä, Jouni Hartikainen, 2011, IEEE Trans. Autom. Control..

Levenberg-Marquardt and Line-Search Extended Kalman Smoothers

Simo Särkkä, Lennart Svensson, S. Särkkä, 2020, ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

On Unscented Kalman Filtering for State Estimation of Continuous-Time Nonlinear Systems

Simo Särkkä, S. Särkkä, 2007, IEEE Trans. Autom. Control..

On Gaussian Optimal Smoothing of Non-Linear State Space Models

Simo Särkkä, Jouni Hartikainen, 2010, IEEE Transactions on Automatic Control.

Partitioned Update Binomial Gaussian Mixture Filter

Ángel F. García-Fernández, Simo Särkkä, Matti Raitoharju, 2019, 2019 22th International Conference on Information Fusion (FUSION).

Pedestrian localization in moving platforms using dead reckoning, particle filtering and map matching

Simo Särkkä, Jarmo Takala, Jussi Collin, 2015, 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

Bayesian ODE Solvers: The Maximum A Posteriori Estimate pdf

Simo Särkkä, Philipp Hennig, Filip Tronarp, 2020, ArXiv.

The 10th annual MLSP competition: First place

Arno Solin, Simo Särkkä, S. Särkkä, 2014, 2014 IEEE International Workshop on Machine Learning for Signal Processing (MLSP).

Combining particle MCMC with Rao-Blackwellized Monte Carlo data association for parameter estimation in multiple target tracking

Simo Särkkä, Juho Kokkala, S. Särkkä, 2014, Digit. Signal Process..

Machine Learning Methods for Neonatal Mortality and Morbidity Classification

Ali Bahrami Rad, Simo Särkkä, Markus Leskinen, 2020, IEEE Access.

On the use of gradient information in Gaussian process quadratures

Simo Särkkä, Jakub Prüher, S. Särkkä, 2016, 2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP).

Batch nonlinear continuous-time trajectory estimation as exactly sparse Gaussian process regression

Simo Särkkä, Tim D. Barfoot, Chi Hay Tong, 2014, Autonomous Robots.

Fast Motion Deblurring for Feature Detection and Matching Using Inertial Measurements

Jiri Matas, Juho Kannala, Simo Särkkä, 2018, 2018 24th International Conference on Pattern Recognition (ICPR).

RFID-based butterfly location sensing system

Ville Viikari, Simo Särkkä, Kaarle Jaakkola, 2014, 2014 22nd European Signal Processing Conference (EUSIPCO).

On the LP-convergence of a Girsanov theorem based particle filter

Eric Moulines, Simo Särkkä, É. Moulines, 2016, 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

On the L4 convergence of particle filters with general importance distributions

Simo Särkkä, Isambi S. Mbalawata, S. Särkkä, 2014, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

Hilbert space methods for reduced-rank Gaussian process regression

Arno Solin, Simo Särkkä, S. Särkkä, 2014, Stat. Comput..

Posterior Linearization Filter: Principles and Implementation Using Sigma Points

Ángel F. García-Fernández, Mark R. Morelande, Simo Särkkä, 2015, IEEE Transactions on Signal Processing.

Variational Bayesian adaptation of noise covariances in multiple target tracking problems

Simo Särkkä, Mohsin M. Jamali, Seyyed Soheil Sadat Hosseini, 2018, Measurement.

Motion Artifact Reduction in Ambulatory Electrocardiography Using Inertial Measurement Units and Kalman Filtering

Roland Hostettler, Simo Särkkä, Tuomo Nieminen, 2018, 2018 21st International Conference on Information Fusion (FUSION).

Student's $t$-Filters for Noise Scale Estimation

Simo Särkkä, Toni Karvonen, Filip Tronarp, 2019, IEEE Signal Processing Letters.

Parameter estimation in stochastic differential equations with Markov chain Monte Carlo and non-linear Kalman filtering

Simo Särkkä, Heikki Haario, Isambi S. Mbalawata, 2013, Comput. Stat..

Symmetry exploits for Bayesian cubature methods

Simo Särkkä, Toni Karvonen, Chris J. Oates, 2018, Statistics and Computing.

On MCMC sampling in Bayesian MLP neural networks

Jouko Lampinen, Aki Vehtari, Simo Särkkä, 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium.

MIXTURE REPRESENTATION OF THE MATÉRN CLASS WITH APPLICATIONS IN STATE SPACE APPROXIMATIONS AND BAYESIAN QUADRATURE

Simo Särkkä, Toni Karvonen, Filip Tronarp, 2018, 2018 IEEE 28th International Workshop on Machine Learning for Signal Processing (MLSP).

Joint estimation of neural sources and their functional connections from MEG data

Simo Särkkä, Lauri Parkkonen, Filip Tronarp, 2020, bioRxiv.

Spatiotemporal Learning via Infinite-Dimensional Bayesian Filtering and Smoothing: A Look at Gaussian Process Regression Through Kalman Filtering

Arno Solin, Simo Särkkä, Jouni Hartikainen, 2013, IEEE Signal Processing Magazine.

Fourier-Hermite Rauch-Tung-Striebel smoother

Simo Särkkä, Juha Sarmavuori, 2012, 2012 Proceedings of the 20th European Signal Processing Conference (EUSIPCO).

Worst-case optimal approximation with increasingly flat Gaussian kernels

Simo Särkkä, Toni Karvonen, 2020, Adv. Comput. Math..

Recursive Noise Adaptive Kalman Filtering by Variational Bayesian Approximations

Simo Särkkä, Aapo Nummenmaa, S. Särkkä, 2009, IEEE Transactions on Automatic Control.

Continuous-Space Gaussian Process Regression and Generalized Wiener Filtering with Application to Learning Curves

Arno Solin, Simo Särkkä, 2013, SCIA.

Adaptive Metropolis algorithm using variational Bayesian adaptive Kalman filter

Simo Särkkä, Heikki Haario, Matti Vihola, 2013, Comput. Stat. Data Anal..

Hilbert-Space Reduced-Rank Methods For Deep Gaussian Processes

Simo Särkkä, Muhammad F. Emzir, Zenith Purisha, 2019, 2019 IEEE 29th International Workshop on Machine Learning for Signal Processing (MLSP).

Rao-Blackwellized particle mcmc for parameter estimation in spatio-temporal Gaussian processes

Simon J. Godsill, Roland Hostettler, Simo Särkkä, 2017, 2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP).

Kernel-based interpolation at approximate Fekete points

Simo Särkkä, Toni Karvonen, Ken'ichiro Tanaka, 2020, Numerical Algorithms.

Gaussian Process Latent Force Models for Learning and Stochastic Control of Physical Systems

Neil D. Lawrence, Simo Särkkä, Mauricio A. Álvarez, 2017, IEEE Transactions on Automatic Control.

Rao-Blackwellized Particle Smoothers for Conditionally Linear Gaussian Models

Simon J. Godsill, Fredrik Lindsten, Thomas B. Schön, 2015, IEEE Journal of Selected Topics in Signal Processing.

Weight moment conditions for L4 convergence of particle filters for unbounded test functions

Simo Särkkä, Isambi S. Mbalawata, S. Särkkä, 2014, 2014 22nd European Signal Processing Conference (EUSIPCO).

Updates in Bayesian Filtering by Continuous Projections on a Manifold of Densities

Simo Särkkä, Filip Tronarp, S. Särkkä, 2019, ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

Sigma-point filtering for nonlinear systems with non-additive heavy-tailed noise

Roland Hostettler, Simo Särkkä, Filip Tronarp, 2016, 2016 19th International Conference on Information Fusion (FUSION).

Sensors and AI Techniques for Situational Awareness in Autonomous Ships: A Review

Simo Särkkä, Zheng Zhao, M. Zahidul H. Bhuiyan, 2020, IEEE Transactions on Intelligent Transportation Systems.

A linear stochastic state space model for electrocardiograms

Simo Särkkä, Kimmo Suotsalo, S. Särkkä, 2017, 2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP).

A probabilistic model for the numerical solution of initial value problems

Simo Särkkä, Philipp Hennig, Michael Schober, 2016, Statistics and Computing.

Respiratory Pattern Recognition from Low-Resolution Thermal Imaging

Simo Särkkä, Ajinkya Gorad, Salla Aario, 2020, ESANN.

Real-Time Tracking of Multiple Acoustical Sources Utilising Rao-Blackwellised Particle Filtering

Simo Särkkä, Archontis Politis, Leo McCormack, 2021, 2021 29th European Signal Processing Conference (EUSIPCO).

Correction to: Kernel-based interpolation at approximate Fekete points

Simo Särkkä, Toni Karvonen, Ken’ichiro Tanaka, 2021, Numer. Algorithms.

Temporal Parallelization of Dynamic Programming and Linear Quadratic Control pdf

Ángel F. García-Fernández, Simo Särkkä, S. Särkkä, 2021, IEEE Transactions on Automatic Control.

Parallel Iterated Extended and Sigma-Point Kalman Smoothers

Simo Särkkä, Fatemeh Yaghoobi, Adrien Corenflos, 2021, ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

Metal Artifact Reduction In Cone-Beam Extremity Images Using Gated Convolutions

Simo Särkkä, Harshit Agrawal, Ari Hietanen, 2021, 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI).

Gaussian Approximations of SDES in Metropolis-Adjusted Langevin Algorithms

Simo Särkkä, Christos Merkatas, Toni Karvonen, 2021, 2021 IEEE 31st International Workshop on Machine Learning for Signal Processing (MLSP).

Temporal Parallelization of Inference in Hidden Markov Models

Ángel F. García-Fernández, Simo Särkkä, Syeda Sakira Hassan, 2021, IEEE Transactions on Signal Processing.

A Probabilistic Taylor Expansion with Applications in Filtering and Differential Equations pdf

Simo Särkkä, Toni Karvonen, Jon Cockayne, 2021, ArXiv.

Deep state-space Gaussian processes

Simo Särkkä, Muhammad F. Emzir, Zheng Zhao, 2020, Statistics and Computing.

Kalman filtering with empirical noise models

Simo Särkkä, Henri Nurminen, Matti Raitoharju, 2021, 2021 International Conference on Localization and GNSS (ICL-GNSS).