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Found 6 papers, 3 papers with code
Simulation-based inference using surjective sequential neural likelihood estimation
We present Surjective Sequential Neural Likelihood (SSNL) estimation, a novel method for simulation-based inference in models where the evaluation of the likelihood function is not tractable and only a simulator that can generate synthetic data is available.
Learning Summary Statistics for Bayesian Inference with Autoencoders
For stochastic models with intractable likelihood functions, approximate Bayesian computation offers a way of approximating the true posterior through repeated comparisons of observations with simulated model outputs in terms of a small set of summary statistics.
Second-order Phase Transition in Phytoplankton Trait Dynamics
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.
Appraisal of data-driven and mechanistic emulators of nonlinear hydrodynamic urban drainage simulators
In this paper, we therefore compare the performance of two families of emulators for open channel flows in the context of urban drainage simulators.
A Simulated Annealing Approach to Bayesian Inference
In the general case, we propose annealing with a constant entropy production rate, which is optimal as long as annealing is not too fast.
A Simulated Annealing Approach to Approximate Bayes Computations
In particle ABC, an ensemble of particles in the product space of model outputs and parameters is propagated in such a way that its output marginal approaches a delta function at the data and its parameter marginal approaches the posterior distribution.
Computation Computational Physics
