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Found 6 papers, 5 papers with code
Testing whether a Learning Procedure is Calibrated
A learning procedure takes as input a dataset and performs inference for the parameters $\theta$ of a model that is assumed to have given rise to the dataset.
Bayesian Inference
Statistics Theory
Statistics Theory
Measure Transport with Kernel Stein Discrepancy
Measure transport underpins several recent algorithms for posterior approximation in the Bayesian context, wherein a transport map is sought to minimise the Kullback--Leibler divergence (KLD) from the posterior to the approximation.
Manifold lifting: scaling MCMC to the vanishing noise regime
Standard Markov chain Monte Carlo methods struggle to explore distributions that are concentrated in the neighbourhood of low-dimensional structures.
Computation
Manifold Markov chain Monte Carlo methods for Bayesian inference in diffusion models
Bayesian inference for nonlinear diffusions, observed at discrete times, is a challenging task that has prompted the development of a number of algorithms, mainly within the computational statistics community.
Computation Methodology 65C40 (Primary) 65C05, 65C40 (Secondary) G.3
Continuously tempered Hamiltonian Monte Carlo
Hamiltonian Monte Carlo (HMC) is a powerful Markov chain Monte Carlo (MCMC) method for performing approximate inference in complex probabilistic models of continuous variables.
Computation
Asymptotically exact inference in differentiable generative models
We use the intuition that inference corresponds to integrating a density across the manifold corresponding to the set of inputs consistent with the observed outputs.
