compops
Business-minded PhD passionate about data, algorithms and improving the world
Kotte Consulting ABStockholm, Sweden
Pinned Repositories
barx-sysid2018
Sparse Bayesian ARX models with flexible noise distributions
gpo-ifac2014
Particle filter-based Gaussian process optimisation for parameter inference
gpo-smc-abc
Bayesian optimisation for fast approximate inference in state-space models with intractable likelihoods
panel-dpm2016
Approximate Bayesian inference for mixed effects models with heterogeneity
phd-thesis
Accelerating Monte Carlo methods for Bayesian inference in dynamical models
pmh-tutorial
Source code and data for the tutorial: "Getting started with particle Metropolis-Hastings for inference in nonlinear models"
pmh-tutorial-rpkg
R package pmhtutorial available from CRAN.
pmmh-correlated2015
Accelerating pseudo-marginal Metropolis-Hastings by correlating auxiliary variables
pmmh-qn
Correlated pseudo-marginal Metropolis-Hastings using quasi-Newton proposals
rjmcmc-sysid2012
Hierarchical Bayesian approaches for robust inference in ARX models
compops's Repositories
compops/pmh-tutorial
Source code and data for the tutorial: "Getting started with particle Metropolis-Hastings for inference in nonlinear models"
compops/gpo-smc-abc
Bayesian optimisation for fast approximate inference in state-space models with intractable likelihoods
compops/gpo-ifac2014
Particle filter-based Gaussian process optimisation for parameter inference
compops/phd-thesis
Accelerating Monte Carlo methods for Bayesian inference in dynamical models
compops/barx-sysid2018
Sparse Bayesian ARX models with flexible noise distributions
compops/pmh-tutorial-rpkg
R package pmhtutorial available from CRAN.
compops/rjmcmc-sysid2012
Hierarchical Bayesian approaches for robust inference in ARX models
compops/panel-dpm2016
Approximate Bayesian inference for mixed effects models with heterogeneity
compops/pmmh-correlated2015
Accelerating pseudo-marginal Metropolis-Hastings by correlating auxiliary variables
compops/newton-sysid2015
Newton-based maximum likelihood estimation in nonlinear state space models
compops/qnmh-sysid2018
Constructing Metropolis-Hastings proposals using damped BFGS updates
compops/smc-toyexample
Sequential Monte Carlo methods (particle filtering/smoothing) for a toy problem
compops/lic-thesis
Source code and data for examples in thesis "Sequential Monte Carlo for inference in nonlinear state space models"
compops/ml-examples
Implementations from a graduate course following "Pattern Recognition and Machine Learning) written by Bishop and published in 2006.
compops/ml-seminar-20180816
compops/pmh-stco2015
Particle Metropolis-Hastings using gradient and Hessian information
compops/pmmh-qn
Correlated pseudo-marginal Metropolis-Hastings using quasi-Newton proposals
compops/eqm-ar
Inference in Gaussian models with missing data using Equalisation Maximisation
compops/pmh-tutorial-seminars
Code skeletons for implementing PMH in MATLAB based on the repo pmh-tutorial
compops/qpmh2-sysid2015
Quasi-Newton particle Metropolis-Hastings