76 citations · 193 across the 9 of their papers we have counts for
10 papers
Simulation-based Inference for High-dimensional Data using Surjective Sequential Neural Likelihood Estimation
Simon Dirmeier, Carlo Albert, Fernando Perez-Cruz
Neural likelihood estimation methods for simulation-based inference can suffer from performance degradation when the modeled data is very high-dimensional or lies along a lower-dim…
Learning Summary Statistics for Bayesian Inference with Autoencoders
Carlo Albert, Simone Ulzega, Firat Ozdemir +2
For stochastic models with intractable likelihood functions, approximate Bayesian computation offers a way of approximating the true posterior through repeated comparisons of obser…
Second-order Phase Transition in Phytoplankton Trait Dynamics
Jenny Held, Tom Lorimer, Francesco Pomati +2
Key traits of unicellular species, like cell size, often follow scale-free or self-similar distributions, hinting at the possibility of an underlying critical process. However, lin…
Accelerating Bayesian inference in hydrological modeling with a mechanistic emulator
David Machac, Peter Reichert, Jörg Rieckermann +2
As in many fields of dynamic modeling, the long runtime of hydrological models hinders Bayesian inference of model parameters from data. By replacing a model with an approximation…
Appraisal of data-driven and mechanistic emulators of nonlinear hydrodynamic urban drainage simulators
Juan Pablo Carbajal, João Paulo Leitão, Carlo Albert +1
Many model based scientific and engineering methodologies, such as system identification, sensitivity analysis, optimization and control, require a large number of model evaluation…
A Simulated Annealing Approach to Bayesian Inference
Carlo Albert
A generic algorithm for the extraction of probabilistic (Bayesian) information about model parameters from data is presented. The algorithm propagates an ensemble of particles in t…
Boosting Bayesian Parameter Inference of Nonlinear Stochastic Differential Equation Models by Hamiltonian Scale Separation
Carlo Albert, Simone Ulzega, Ruedi Stoop
Parameter inference is a fundamental problem in data-driven modeling. Given observed data that is believed to be a realization of some parameterized model, the aim is to find param…
A Simulated Annealing Approach to Approximate Bayes Computations
Carlo Albert, Hans R. Kuensch, Andreas Scheidegger
Approximate Bayes Computations (ABC) are used for parameter inference when the likelihood function of the model is expensive to evaluate but relatively cheap to sample from. In par…
A Mechanistic Dynamic Emulator
Carlo Albert
In applied sciences, we often deal with deterministic simulation models that are too slow for simulation-intensive tasks such as calibration or real-time control. In this paper, an…
Batalin-Vilkovisky Integrals in Finite Dimensions
Carlo Albert, Bea Bleile, Jürg Fröhlich
The Batalin-Vilkovisky method (BV) is the most powerful method to analyze functional integrals with (infinite-dimensional) gauge symmetries presently known. It has been invented to…