
Markovian Score Climbing: Variational Inference with KL(pq)
Modern variational inference (VI) uses stochastic gradients to avoid int...
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A general framework for ensemble distribution distillation
Ensembles of neural networks have been shown to give better performance ...
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Deep Gaussian Markov random fields
Gaussian Markov random fields (GMRFs) are probabilistic graphical models...
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Parameter elimination in particle Gibbs sampling
Bayesian inference in statespace models is challenging due to highdime...
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Calibration tests in multiclass classification: A unifying framework
In safetycritical applications a probabilistic model is usually require...
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Particle filter with rejection control and unbiased estimator of the marginal likelihood
We consider the combined use of resampling and partial rejection control...
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Elements of Sequential Monte Carlo
A core problem in statistics and probabilistic machine learning is to co...
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Evaluating model calibration in classification
Probabilistic classifiers output a probability distribution on target cl...
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Constructing the Matrix Multilayer Perceptron and its Application to the VAE
Like most learning algorithms, the multilayer perceptrons (MLP) is desig...
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Graphical model inference: Sequential Monte Carlo meets deterministic approximations
Approximate inference in probabilistic graphical models (PGMs) can be gr...
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Learning dynamical systems with particle stochastic approximation EM
We present the particle stochastic approximation EM (PSAEM) algorithm fo...
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Improving the particle filter for highdimensional problems using artificial process noise
The particle filter is one of the most successful methods for state infe...
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Learning nonlinear statespace models using smooth particlefilterbased likelihood approximations
When classical particle filtering algorithms are used for maximum likeli...
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Pseudoextended Markov chain Monte Carlo
Sampling from the posterior distribution using Markov chain Monte Carlo ...
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Learning of statespace models with highly informative observations: a tempered Sequential Monte Carlo solution
Probabilistic (or Bayesian) modeling and learning offers interesting pos...
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Highdimensional Filtering using Nested Sequential Monte Carlo
Sequential Monte Carlo (SMC) methods comprise one of the most successful...
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PseudoMarginal Hamiltonian Monte Carlo
Bayesian inference in the presence of an intractable likelihood function...
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Interacting Particle Markov Chain Monte Carlo
We introduce interacting particle Markov chain Monte Carlo (iPMCMC), a P...
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Accelerating pseudomarginal MetropolisHastings by correlating auxiliary variables
Pseudomarginal MetropolisHastings (pmMH) is a powerful method for Baye...
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Sequential Monte Carlo Methods for System Identification
One of the key challenges in identifying nonlinear and possibly nonGaus...
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QuasiNewton particle MetropolisHastings
Particle MetropolisHastings enables Bayesian parameter inference in gen...
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Nested Sequential Monte Carlo Methods
We propose nested sequential Monte Carlo (NSMC), a methodology to sample...
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Sequential Kernel Herding: FrankWolfe Optimization for Particle Filtering
Recently, the FrankWolfe optimization algorithm was suggested as a proc...
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Identification of jump Markov linear models using particle filters
Jump Markov linear models consists of a finite number of linear state sp...
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Sequential Monte Carlo for Graphical Models
We propose a new framework for how to use sequential Monte Carlo (SMC) a...
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Particle Gibbs with Ancestor Sampling
Particle Markov chain Monte Carlo (PMCMC) is a systematic way of combini...
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Identification of Gaussian Process StateSpace Models with Particle Stochastic Approximation EM
Gaussian process statespace models (GPSSMs) are a very flexible family...
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Bayesian Inference and Learning in Gaussian Process StateSpace Models with Particle MCMC
Statespace models are successfully used in many areas of science, engin...
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Ancestor Sampling for Particle Gibbs
We present a novel method in the family of particle MCMC methods that we...
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Fredrik Lindsten
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Senior Lecturer at the Department of Information Technology, Uppsala University, Owner & Consultant at Inferentic, Postdoctoral Research Associate at Department of Engineering at the University of Cambridge from 20142015, PhD Student at Linköping University from 20082013, Visiting Student Researcher at UC Berkeley 2012, Master Thesis at Saab Aerosystems 2008.