Probabilistic programming in Python using PyMC3

Probabilistic programming better waters xl7000 allows for automatic Bayesian inference on user-defined probabilistic models.Recent advances in Markov chain Monte Carlo (MCMC) sampling allow inference on increasingly complex models.This class of MCMC, known as Hamiltonian Monte Carlo, requires gradient information which is often not readily availabl

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A Comparison Study of Explicit and Implicit 3-D Transient Electromagnetic Forward Modeling Schemes on Multi-Resolution Grid

This study compares the efficiency of 3-D la tierra de acre mezcal transient electromagnetic forward modeling schemes on the multi-resolution grid for various modeling scenarios.We developed time-domain finite-difference modeling based on the explicit scheme earlier.In this work, we additionally implement 3-D transient electromagnetic forward model

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