Markov chain Monte Carlo (MCMC) methods have been used extensively in statistical physics over the last 40 years, in spatial statistics for the past 20 and in Bayesian image analysis over the last decade. In the last five years, MCMC has been introduced into significance testing, ...
[Bayesian Computation and Stochastic Systems]: Rejoinder Markov chain Monte Carlo (MCMC) methods have been used extensively in statistical physics over the last 40 years, in spatial statistics for the past 20 and in Bayesian image analysis over the last decade. In the last five years, MCMC has...
Approximate Bayesian Computation (ABC) and other simulation-basedinference methods are becoming increasingly used for inference incomplex systems, due to their relative ease-of-implementation. We brieflyreview some of the more popular variants of ABC and their applicationin epidemiology, before using a ...
Besag J, Green P, Higdon D, Mengersen K (1995) Bayesian computation and stochastic systems. Stat Sci 10(1): 3–41 ArticleGoogle Scholar Billheimer D, Cardoso T, Freeman E, Guttorp P, Ko H, Silkey M (1997) Natural variability of benthic species composition in the Delaware Bay. Environ...
Approximate Bayesian Computation for Astronomical Model Analysis: A Case Study in Galaxy Demographics and Morphological Transformation at High Redshift Approximate Bayesian Computation (ABC) represents a powerful methodology for the analysis of complex stochastic systems for which the likelihood of the obs....
The computation holds in the thermodynamic limit, where both Nℓ and P are large and their ratio αℓ = P/Nℓ is finite. This advance allows us to obtain: (1) a closed formula for the generalization error associated with a regression task in a one-hidden layer network with ...
Approximate Bayesian computation schemes for parameter inference of discrete stochastic models using simulated likelihood density Mathematical modeling is an important tool in systems biology to study the dynamic property of complex biological systems. However, one of the major challe... Q Wu,K Smith-Mi...
The sum of probabilities for each possible genotype is computed and used as the prior probabilities for the next generation. Show moreView article A critical review of common pitfalls and guidelines to effectively infer parameters of agent-based models using Approximate Bayesian Computation Lander De ...
[21] developed a user-friendly Python package Abrox for approximate Bayesian computation with a focus on model comparison. There are also Python packages BAMSE [22], BayesPy [23], PyMC [24] and so on. Moreover, Vanhatalo et al. [25] developed the MATLAB toolbox GPstuff for Bayesian ...
Optimizing parameterized quantum circuits is a key routine in using near-term quantum devices. However, the existing algorithms for such optimization require an excessive number of quantum-measurement shots for estimating expectation values of observable