Advances in plant imaging and computer vision have transformed agriculture and biology by enabling continuous and objective trait quantification. However, monitoring large plant populations or ...

Abstract: In the context of Bayesian inversion for scientific and engineering modeling, Markov chain Monte Carlo (MCMC) sampling strategies have become the benchmark due to their flexibility and ...

Nested sampling (NS) has emerged as a powerful tool for exploring thermodynamic properties in materials science. However, its efficiency is often hindered by the limitations of Markov chain Monte ...

This is a Python script using Markov Chain Monte Carlo (MCMC) sampling with a neural network surrogate model to predict colloidal interaction parameters (effective charge and Debye length) from a ...

I am getting an error: "MCMCSamplingError: MCMC sampling failed with a maximum R-hat value of 95825300553728.0." while I am lowering the sample priors I am getting 102240094257152.0. There have been ...

Sampling from probability distributions with known density functions (up to normalization) is a fundamental challenge across various scientific domains. From Bayesian uncertainty quantification to ...

Abstract: The discrete nature of transmitted symbols poses challenges for achieving optimal detection in multiple-input multiple-output (MIMO) systems associated with a large number of antennas.

MCMC Auto - East Belknap offers used imported and domestic cars. Its inventory includes brands like Chevrolet, Dodge, Ford, Hyundai, Kia and Nissan. The dealership provides financing for all credit ...

The authors proposed an important novel deep-learning framework to estimate posterior distributions of tissue microstructure parameters. The method shows superior performance to conventional Bayesian ...