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Found 4 papers, 0 papers with code
Efficient structure learning with automatic sparsity selection for causal graph processes
We propose a novel algorithm for efficiently computing a sparse directed adjacency matrix from a group of time series following a causal graph process.
Probabilistic Linear Multistep Methods
We present a derivation and theoretical investigation of the Adams-Bashforth and Adams-Moulton family of linear multistep methods for solving ordinary differential equations, starting from a Gaussian process (GP) framework.
Sparse Approximate Manifolds for Differential Geometric MCMC
One of the enduring challenges in Markov chain Monte Carlo methodology is the development of proposal mechanisms to make moves distant from the current point, that are accepted with high probability and at low computational cost.
Accelerating Bayesian Inference over Nonlinear Differential Equations with Gaussian Processes
We demonstrate the speed and statistical accuracy of our approach using examples of both ordinary and delay differential equations, and provide a comprehensive comparison with current state of the art methods.
