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Marginalized particle filters for mixed linear/nonlinear state-space models
TLDR
The derivation of the details for the marginalized particle filter for a general nonlinear state-space model is derived and it is demonstrated that the complete high-dimensional system can be based on a particle filter using marginalization for all but three states. Expand
System identification of nonlinear state-space models
TLDR
A Maximum Likelihood (ML) framework is employed and an Expectation Maximisation (EM) algorithm is derived to compute these ML estimates, which lend itself perfectly to the particle smoother, which provides arbitrarily good estimates. Expand
Particle gibbs with ancestor sampling
TLDR
PGAS provides the data analyst with an off-the-shelf class of Markov kernels that can be used to simulate, for instance, the typically high-dimensional and highly autocorrelated state trajectory in a state-space model. Expand
Using Inertial Sensors for Position and Orientation Estimation
In recent years, micro-machined electromechanical system (MEMS) inertial sensors (3D accelerometers and 3D gyroscopes) have become widely available due to their small size and low cost. Inertial seExpand
A Basic Convergence Result for Particle Filtering
TLDR
The basic nonlinear filtering problem for dynamical systems is considered, and a general framework, including many of the particle filter algorithms as special cases, is given. Expand
Evaluating Scalable Bayesian Deep Learning Methods for Robust Computer Vision
TLDR
This work proposes a comprehensive evaluation framework for scalable epistemic uncertainty estimation methods in deep learning and applies this framework to provide the first properly extensive and conclusive comparison of the two current state-of-the- art scalable methods: ensembling and MC-dropout. Expand
Evaluating model calibration in classification
TLDR
This work develops a general theoretical calibration evaluation framework grounded in probability theory, and points out subtleties present in model calibration evaluation that lead to refined interpretations of existing evaluation techniques. Expand
Rao-Blackwellized Particle Smoothers for Conditionally Linear Gaussian Models
TLDR
This work presents a forward-backward-type Rao-Blackwellized particle smoother (RBPS) that is able to exploit the tractable substructure present in these models, and marginalizes out a conditionally tractable subset of state variables. Expand
Ancestor Sampling for Particle Gibbs
TLDR
This work presents a novel method in the family of particle MCMC methods that it refers to as particle Gibbs with ancestor sampling (PG-AS), and develops a truncation strategy of these models that is applicable in principle to any backward-simulation-based method, but which is particularly well suited to the PG-AS framework. Expand
An optimization-based approach to human body motion capture using inertial sensors
In inertial human motion capture, a multitude of body segments are equipped with inertial measurement units, consisting of 3D accelerometers, 3D gyroscopes and 3D magnetometers. Relative position andExpand
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