This leading textbook provides a comprehensive introduction to the fields of pattern recognition and machine learning. It is aimed at advanced undergraduates or first-year PhD students, as well as ...

Now you can also use Bayesian methods to obtain uncertainty within a calibration mapping mainly in the netcal.scaling package. We adapted Markov-Chain Monte-Carlo sampling (MCMC) as well as ...

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 ...

Offline reinforcement learning (RL) is an attractive method that learns a policy purely from a previously collected dataset without additional interaction. However, it suffers the data quality issue ...

Electrical capacitance tomography is targeted on estimating the spatial permittivity distribution of an inhomogeneous medium from measurements of trans-capacitance of a multi-electrode assembly ...

Learn how to compare ML models using bootstrap resampling with a hands-on sklearn implementation. Gov. Whitmer Responds as Trump Considers Kidnap Plot Pardon Nathan Fielder blasts ‘dumb’ FAA ...