Moritz Schauer
Showing 28 publications
Guided smoothing and control for diffusion processes
Compositionality in algorithms for smoothing
Controlled stochastic processes for simulated annealing
Simulating conditioned diffusions on manifolds
Score matching for bridges without learning time-reversals
Score matching for bridges without time-reversals
Causal structure learning with momentum: Sampling distributions over Markov Equivalence Classes
Differentiating Metropolis-Hastings to Optimize Intractable Densities
Conditioning continuous-time Markov processes by guiding
Nonparametric Bayesian volatility learning under microstructure noise
Weak solutions to gamma-driven stochastic differential equations
Sticky PDMP samplers for sparse and local inference problems
Flexible group fairness metrics for survival analysis
Nonparametric Bayesian volatility estimation for gamma-driven stochastic differential equations
Applied measure theory for probabilistic modeling.
Automatic Differentiation of Programs with Discrete Randomness
Diffusion Bridges for Stochastic Hamiltonian Systems and Shape Evolutions
Continuous-discrete smoothing of diffusions
A piecewise deterministic Monte Carlo method for diffusion bridges
Nonparametric Bayesian estimation of a Hölder continuous diffusion coefficient
Simulation of elliptic and hypo-elliptic conditional diffusions
Bayesian wavelet de-noising with the caravan prior
Fast and scalable non-parametric Bayesian inference for Poisson point processes
Nonparametric Bayesian volatility estimation
Nonparametric Bayesian inference for Gamma-type Lévy subordinators
Adaptive nonparametric drift estimation for diffusion processes using Faber–Schauder expansions
Guided Proposals for simulating multi-dimensional diffusion bridges
Reversible jump MCMC for nonparametric drift estimation for diffusion processes
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Showing 6 research projects
Stochastic modelling of interacting shapes: from sweat droplets to tree canopies
Causal inference with generative neural network models
Fast Bayesian Inference with Piecewise Deterministic Markov Processes
Inference in the face of intractability: Bayesian applications of continuous-time Markov processes
Stochastic Continuous-Depth Neural Networks