Sequential Bayesian Inference
8 papers with code • 0 benchmarks • 0 datasets
Also known as Bayesian filtering or recursive Bayesian estimation, this task aims to perform inference on latent state-space models.
Benchmarks
These leaderboards are used to track progress in Sequential Bayesian Inference
Most implemented papers
Particle Flow Bayes' Rule
xinshi-chen/ParticleFlowBayesRule
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We present a particle flow realization of Bayes' rule, where an ODE-based neural operator is used to transport particles from a prior to its posterior after a new observation.
Kernel embedding of maps for sequential Bayesian inference: The variational mapping particle filter
In this work, a novel sequential Monte Carlo filter is introduced which aims at efficient sampling of high-dimensional state spaces with a limited number of particles.
A Discriminative Approach to Bayesian Filtering with Applications to Human Neural Decoding
Given a stationary state-space model that relates a sequence of hidden states and corresponding measurements or observations, Bayesian filtering provides a principled statistical framework for inferring the posterior distribution of the current state given all measurements up to the present time.
Functional Regularisation for Continual Learning with Gaussian Processes
We introduce a framework for Continual Learning (CL) based on Bayesian inference over the function space rather than the parameters of a deep neural network.
The Discriminative Kalman Filter for Bayesian Filtering with Nonlinear and Nongaussian Observation Models
Extensions to the Kalman filter, including the extended and unscented Kalman filters, incorporate linearizations for models where the observation model p(observation∣state) is nonlinear.
Discriminative Bayesian filtering lends momentum to the stochastic Newton method for minimizing log-convex functions
To minimize the average of a set of log-convex functions, the stochastic Newton method iteratively updates its estimate using subsampled versions of the full objective's gradient and Hessian.
On Sequential Bayesian Inference for Continual Learning
Sequential Bayesian inference can be used for continual learning to prevent catastrophic forgetting of past tasks and provide an informative prior when learning new tasks.
A digital twin framework for civil engineering structures
This work proposes a predictive digital twin approach to the health monitoring, maintenance, and management planning of civil engineering structures.
