Andreas Lindholm

Learning dynamical systems with particle stochastic approximation EM

Lindholm, Andreas; Lindsten, Fredrik

Ingår i Foundations of Data Science, s. 1089-1116, 2025

• DOI för Learning dynamical systems with particle stochastic approximation EM

Predicting Political Violence Using a State-Space Model

Lindholm, Andreas; Hendriks, Johannes; Wills, Adrian; Schön, Thomas B.

Ingår i International Interactions, s. 759-777, 2022

• DOI för Predicting Political Violence Using a State-Space Model

Data Consistency Approach to Model Validation

Lindholm, Andreas; Zachariah, Dave; Stoica, Peter; Schön, Thomas B.

Ingår i IEEE Access, s. 59788-59796, 2019

• DOI för Data Consistency Approach to Model Validation
• Ladda ner fulltext (pdf) av Data Consistency Approach to Model Validation

Identification of a Duffing oscillator using particle Gibbs with ancestor sampling

Rogers, T J; Schön, Thomas B.; Lindholm, Andreas; Worden, K et al.

Ingår i Journal of Physics, Conference Series, 2019

• DOI för Identification of a Duffing oscillator using particle Gibbs with ancestor sampling
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Probabilistic learning of nonlinear dynamical systems using sequential Monte Carlo

Schön, Thomas B.; Svensson, Andreas; Murray, Lawrence; Lindsten, Fredrik

Ingår i Mechanical systems and signal processing, s. 866-883, 2018

• DOI för Probabilistic learning of nonlinear dynamical systems using sequential Monte Carlo

Learning dynamical systems with particle stochastic approximation EM

Lindholm, Andreas; Lindsten, Fredrik

Ingår i Foundations of Data Science, s. 1089-1116, 2025

• DOI för Learning dynamical systems with particle stochastic approximation EM

Predicting Political Violence Using a State-Space Model

Lindholm, Andreas; Hendriks, Johannes; Wills, Adrian; Schön, Thomas B.

Ingår i International Interactions, s. 759-777, 2022

• DOI för Predicting Political Violence Using a State-Space Model

Data Consistency Approach to Model Validation

Lindholm, Andreas; Zachariah, Dave; Stoica, Peter; Schön, Thomas B.

Ingår i IEEE Access, s. 59788-59796, 2019

• DOI för Data Consistency Approach to Model Validation
• Ladda ner fulltext (pdf) av Data Consistency Approach to Model Validation

Identification of a Duffing oscillator using particle Gibbs with ancestor sampling

Rogers, T J; Schön, Thomas B.; Lindholm, Andreas; Worden, K et al.

Ingår i Journal of Physics, Conference Series, 2019

• DOI för Identification of a Duffing oscillator using particle Gibbs with ancestor sampling
• Ladda ner fulltext (pdf) av Identification of a Duffing oscillator using particle Gibbs with ancestor sampling

Probabilistic learning of nonlinear dynamical systems using sequential Monte Carlo

Schön, Thomas B.; Svensson, Andreas; Murray, Lawrence; Lindsten, Fredrik

Ingår i Mechanical systems and signal processing, s. 866-883, 2018

• DOI för Probabilistic learning of nonlinear dynamical systems using sequential Monte Carlo

Learning of state-space models with highly informative observations: A tempered sequential Monte Carlo solution

Svensson, Andreas; Schön, Thomas B.; Lindsten, Fredrik

Ingår i Mechanical systems and signal processing, s. 915-928, 2018

• DOI för Learning of state-space models with highly informative observations: A tempered sequential Monte Carlo solution

Probabilistic forecasting of electricity consumption, photovoltaic power generation and net demand of an individual building using Gaussian Processes

van der Meer, Dennis W.; Shepero, Mahmoud; Svensson, Andreas; Widén, Joakim et al.

Ingår i Applied Energy, s. 195-207, 2018

• DOI för Probabilistic forecasting of electricity consumption, photovoltaic power generation and net demand of an individual building using Gaussian Processes

A flexible state–space model for learning nonlinear dynamical systems

Svensson, Andreas; Schön, Thomas B.

Ingår i Automatica, s. 189-199, 2017

• DOI för A flexible state–space model for learning nonlinear dynamical systems

Machine learning with state-space models, Gaussian processes and Monte Carlo methods

Svensson, Andreas

2018

• Ladda ner fulltext (pdf) av Machine learning with state-space models, Gaussian processes and Monte Carlo methods

Learning nonlinear state-space models using smooth particle-filter-based likelihood approximations

Svensson, Andreas; Lindsten, Fredrik; Schön, Thomas B.

s. 652-657, 2018

• DOI för Learning nonlinear state-space models using smooth particle-filter-based likelihood approximations

How consistent is my model with the data?: Information-theoretic model check

Svensson, Andreas; Zachariah, Dave; Schön, Thomas B.

s. 407-412, 2018

• DOI för How consistent is my model with the data?: Information-theoretic model check

Computationally Efficient Bayesian Learning of Gaussian Process State Space Models

Svensson, Andreas; Solin, Arno; Särkkä, Simo; Schön, Thomas B.

Ingår i Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, s. 213-221, 2016

Marginalizing Gaussian process hyperparameters using sequential Monte Carlo

Svensson, Andreas; Dahlin, Johan; Schön, Thomas B.

Ingår i Proc. 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, s. 477-480, 2015

• DOI för Marginalizing Gaussian process hyperparameters using sequential Monte Carlo
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Nonlinear state space smoothing using the conditional particle filter

Svensson, Andreas; Schön, Thomas B.; Kok, Manon

Ingår i Proc. 17th IFAC Symposium on System Identification, s. 975-980, 2015

• DOI för Nonlinear state space smoothing using the conditional particle filter

Nonlinear state space model identification using a regularized basis function expansion

Svensson, Andreas; Schön, Thomas B.; Solin, Arno; Särkkä, Simo

Ingår i Proc. 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, s. 481-484, 2015

• DOI för Nonlinear state space model identification using a regularized basis function expansion

Identification of jump Markov linear models using particle filters

Svensson, Andreas; Schön, Thomas B.; Lindsten, Fredrik

Ingår i Proc. 53rd Conference on Decision and Control, s. 6504-6509, 2014

• DOI för Identification of jump Markov linear models using particle filters

Learning probabilistic models of dynamical phenomena using particle filters

Svensson, Andreas

2016

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Comparing two recent particle filter implementations of Bayesian system identification

Svensson, Andreas; Schön, Thomas B.

2016

• Ladda ner fulltext (pdf) av Comparing two recent particle filter implementations of Bayesian system identification