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07/20/2023
Graphs in State-Space Models for Granger Causality in Climate Science
Granger causality (GC) is often considered not an actual form of causali...
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07/06/2023
Sparse Graphical Linear Dynamical Systems
Time-series datasets are central in numerous fields of science and engin...
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06/20/2023
Sparse Bayesian Estimation of Parameters in Linear-Gaussian State-Space Models
State-space models (SSMs) are a powerful statistical tool for modelling ...
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03/22/2023
GraphIT: Iterative reweighted â„“_1 algorithm for sparse graph inference in state-space models
State-space models (SSMs) are a common tool for modeling multi-variate d...
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02/20/2023
Differentiable Bootstrap Particle Filters for Regime-Switching Models
Differentiable particle filters are an emerging class of particle filter...
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11/09/2022
Regularized Rényi divergence minimization through Bregman proximal gradient algorithms
We study the variational inference problem of minimizing a regularized R...
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10/19/2022
Gradient-based Adaptive Importance Samplers
Importance sampling (IS) is a powerful Monte Carlo methodology for the a...
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10/03/2022
Efficient Bayes Inference in Neural Networks through Adaptive Importance Sampling
Bayesian neural networks (BNNs) have received an increased interest in t...
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09/30/2022
Learning with MISELBO: The Mixture Cookbook
Mixture models in variational inference (VI) is an active field of resea...
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09/27/2022
Hamiltonian Adaptive Importance Sampling
Importance sampling (IS) is a powerful Monte Carlo (MC) methodology for ...
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09/20/2022
Graphical Inference in Linear-Gaussian State-Space Models
State-space models (SSM) are central to describe time-varying complex sy...
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07/09/2022
Variance Analysis of Multiple Importance Sampling Schemes
Multiple importance sampling (MIS) is an increasingly used methodology w...
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05/15/2022
Large Data and (Not Even Very) Complex Ecological Models: When Worlds Collide
We consider the challenges that arise when fitting complex ecological mo...
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04/14/2022
Optimized Population Monte Carlo
Adaptive importance sampling (AIS) methods are increasingly used for the...
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03/25/2022
A Point Mass Proposal Method for Bayesian State-Space Model Fitting
State-space models (SSMs) are often used to model time series data where...
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02/22/2022
Multiple Importance Sampling ELBO and Deep Ensembles of Variational Approximations
In variational inference (VI), the marginal log-likelihood is estimated ...
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08/30/2021
A principled stopping rule for importance sampling
Importance sampling (IS) is a Monte Carlo technique that relies on weigh...
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07/18/2021
Compressed particle methods for expensive models with application in Astronomy and Remote Sensing
In many inference problems, the evaluation of complex and costly models ...
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07/18/2021
Compressed Monte Carlo with application in particle filtering
Bayesian models have become very popular over the last years in several ...
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02/10/2021
Advances in Importance Sampling
Importance sampling (IS) is a Monte Carlo technique for the approximatio...
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11/18/2020
Optimized Auxiliary Particle Filters
Auxiliary particle filters (APFs) are a class of sequential Monte Carlo ...
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01/09/2020
GraphEM: EM algorithm for blind Kalman filtering under graphical sparsity constraints
Modeling and inference with multivariate sequences is central in a numbe...
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01/09/2020
Importance Gaussian Quadrature
Importance sampling (IS) and numerical integration methods are usually e...
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11/04/2019
New results on particle filters with adaptive number of particles
In this paper, we present new results on particle filters with adaptive ...
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09/23/2019
Approximate Shannon Sampling in Importance Sampling: Nearly Consistent Finite Particle Estimates
In Bayesian inference, we seek to compute information about random varia...
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01/13/2019
Multiple Importance Sampling for Efficient Symbol Error Rate Estimation
Digital constellations formed by hexagonal or other non-square two-dimen...
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12/04/2018
A probabilistic incremental proximal gradient method
In this paper, we propose a probabilistic optimization method, named pro...
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09/11/2018
Rethinking the Effective Sample Size
The effective sample size (ESS) is widely used in sample-based simulatio...
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07/12/2018
The Incremental Proximal Method: A Probabilistic Perspective
In this work, we highlight a connection between the incremental proximal...
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05/31/2018
Robust Covariance Adaptation in Adaptive Importance Sampling
Importance sampling (IS) is a Monte Carlo methodology that allows for ap...
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04/08/2018
Accelerating MCMC Algorithms
Markov chain Monte Carlo algorithms are used to simulate from complex st...
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02/16/2018
Pattern Localization in Time Series through Signal-To-Model Alignment in Latent Space
In this paper, we study the problem of locating a predefined sequence of...
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04/15/2017
Metropolis Sampling
Monte Carlo (MC) sampling methods are widely applied in Bayesian inferen...
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09/11/2016
On the Relationship between Online Gaussian Process Regression and Kernel Least Mean Squares Algorithms
We study the relationship between online Gaussian process (GP) regressio...
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01/27/2015
