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Kotte Consulting AB
- Stockholm, Sweden
- https://www.kotteconsulting.se/
- https://orcid.org/0000-0002-9424-1272
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pmh-tutorial-rpkg Public
R package pmhtutorial available from CRAN.
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pmh-tutorial Public
Source code and data for the tutorial: "Getting started with particle Metropolis-Hastings for inference in nonlinear models"
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qnmh-sysid2018 Public
Constructing Metropolis-Hastings proposals using damped BFGS updates
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pmmh-qn Public
Correlated pseudo-marginal Metropolis-Hastings using quasi-Newton proposals
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barx-sysid2018 Public
Sparse Bayesian ARX models with flexible noise distributions
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panel-dpm2016 Public
Approximate Bayesian inference for mixed effects models with heterogeneity
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rjmcmc-sysid2012 Public
Hierarchical Bayesian approaches for robust inference in ARX models
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phd-thesis Public
Accelerating Monte Carlo methods for Bayesian inference in dynamical models
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lic-thesis Public
Source code and data for examples in thesis "Sequential Monte Carlo for inference in nonlinear state space models"
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gpo-smc-abc Public
Bayesian optimisation for fast approximate inference in state-space models with intractable likelihoods
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qpmh2-sysid2015 Public
Quasi-Newton particle Metropolis-Hastings
Python GNU General Public License v3.0 UpdatedNov 29, 2017 -
pmmh-correlated2015 Public
Accelerating pseudo-marginal Metropolis-Hastings by correlating auxiliary variables
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eqm-ar Public
Inference in Gaussian models with missing data using Equalisation Maximisation
MATLAB GNU General Public License v3.0 UpdatedNov 29, 2017 -
gpo-ifac2014 Public
Particle filter-based Gaussian process optimisation for parameter inference
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pmh-stco2015 Public
Particle Metropolis-Hastings using gradient and Hessian information
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newton-sysid2015 Public
Newton-based maximum likelihood estimation in nonlinear state space models
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pmh-tutorial-seminars Public
Code skeletons for implementing PMH in MATLAB based on the repo pmh-tutorial
monte-carlo matlab particle-filter system-identification state-space-model particle-metropolis-hastingsMATLAB GNU General Public License v3.0 UpdatedOct 3, 2017 -
smc-toyexample Public
Sequential Monte Carlo methods (particle filtering/smoothing) for a toy problem
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ml-examples Public
Implementations from a graduate course following "Pattern Recognition and Machine Learning) written by Bishop and published in 2006.