Inference via low-dimensional couplings
We investigate the low-dimensional structure of deterministic transformations between
random variables, ie, transport maps between probability measures. In the context of …
random variables, ie, transport maps between probability measures. In the context of …
Residual-based error correction for neural operator accelerated infinite-dimensional Bayesian inverse problems
We explore using neural operators, or neural network representations of nonlinear maps
between function spaces, to accelerate infinite-dimensional Bayesian inverse problems …
between function spaces, to accelerate infinite-dimensional Bayesian inverse problems …
Some models are useful, but how do we know which ones? Towards a unified Bayesian model taxonomy
Probabilistic (Bayesian) modeling has experienced a surge of applications in almost all
quantitative sciences and industrial areas. This development is driven by a combination of …
quantitative sciences and industrial areas. This development is driven by a combination of …
An introduction to sampling via measure transport
We present the fundamentals of a measure transport approach to sampling. The idea is to
construct a deterministic coupling---ie, a transport map---between a complex" target" …
construct a deterministic coupling---ie, a transport map---between a complex" target" …
Principal component analysis and sparse polynomial chaos expansions for global sensitivity analysis and model calibration: Application to urban drainage simulation
This paper presents an efficient surrogate modeling strategy for the uncertainty
quantification and Bayesian calibration of a hydrological model. In particular, a process …
quantification and Bayesian calibration of a hydrological model. In particular, a process …
Inverse problems in a Bayesian setting
In a Bayesian setting, inverse problems and uncertainty quantification (UQ)—the
propagation of uncertainty through a computational (forward) model—are strongly …
propagation of uncertainty through a computational (forward) model—are strongly …
Gibbs flow for approximate transport with applications to Bayesian computation
Let π 0 and π 1 be two distributions on the Borel space (R d, B (R d)). Any measurable
function T: R d→ R d such that Y= T (X)∼ π 1 if X∼ π 0 is called a transport map from π 0 to …
function T: R d→ R d such that Y= T (X)∼ π 1 if X∼ π 0 is called a transport map from π 0 to …
Beyond normality: Learning sparse probabilistic graphical models in the non-Gaussian setting
R Morrison, R Baptista… - Advances in neural …, 2017 - proceedings.neurips.cc
We present an algorithm to identify sparse dependence structure in continuous and non-
Gaussian probability distributions, given a corresponding set of data. The conditional …
Gaussian probability distributions, given a corresponding set of data. The conditional …
Scalable Bayesian transport maps for high-dimensional non-Gaussian spatial fields
M Katzfuss, F Schäfer - Journal of the American Statistical …, 2024 - Taylor & Francis
A multivariate distribution can be described by a triangular transport map from the target
distribution to a simple reference distribution. We propose Bayesian nonparametric …
distribution to a simple reference distribution. We propose Bayesian nonparametric …
Heavy-tailed sampling via transformed unadjusted Langevin algorithm
We analyze the oracle complexity of sampling from polynomially decaying heavy-tailed
target densities based on running the Unadjusted Langevin Algorithm on certain …
target densities based on running the Unadjusted Langevin Algorithm on certain …