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Found 8 papers, 1 papers with code
Deep State-Space Model for Predicting Cryptocurrency Price
We call the proposed approach the deep state-space model.
Sparse Graphical Linear Dynamical Systems
This work proposes a novel approach to fill this gap by introducing a joint graphical modeling framework that bridges the static graphical Lasso model and a causal-based graphical approach for the linear-Gaussian SSM.
Bayesian data fusion with shared priors
When data is only available in a distributed fashion or when different sensors are used to infer a quantity of interest, data fusion becomes essential.
Efficient Bayes Inference in Neural Networks through Adaptive Importance Sampling
Bayesian neural networks (BNNs) have received an increased interest in the last years.
The Incremental Proximal Method: A Probabilistic Perspective
We then carry out this observation to a general sequential setting: We consider the incremental proximal method, which is an algorithm for large-scale optimization, and show that, for a linear-quadratic cost function, it can naturally be realized by the Kalman filter.
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 patterns in a time series.
Metropolis Sampling
Monte Carlo (MC) sampling methods are widely applied in Bayesian inference, system simulation and optimization problems.
The Recycling Gibbs Sampler for Efficient Learning
The key point for the successful application of the Gibbs sampler is the ability to draw efficiently samples from the full-conditional probability density functions.
