Probabilistic models, such as hidden Markov models or Bayesian networks, are commonly used to model biological data. Much of their popularity can be attributed to the existence of efficient and robust ...

Abstract: The leaf area index (LAI) is a biophysical variable related to atmosphere-biosphere exchange of CO 2. One way to obtain LAI value is by the Moderate Resolution Imaging Spectroradiometer ...

This package contains functions implementing the BrainMap model proposed in Mejia et al. (2019) and the spatial BrainMap model proposed in proposed in Mejia et al. (2020+). (Previously, these models ...

Bayesian regression with linear basis function models. Introduction to Bayesian linear regression. Implementation with plain NumPy and scikit-learn. See also PyMC3 implementation. Gaussian processes.

In recent years, a learning method for classifiers using tensor networks (TNs) has attracted attention. When constructing a classification function for high-dimensional data using a basis function ...

This paper examines the utilization of machine learning methods to predict the values of valuable metals, specifically gold, silver, palladium, and platinum, from 2017 to 2023. Accurate price ...

Abstract: Efficient learning and model compression algorithm for deep neural network (DNN) is a key workhorse behind the rise of deep learning (DL). In this work, we propose a message passing-based ...

Randomized controlled trials are considered the golden standard for estimating treatment effect but are costly to perform and not always possible. Observational data, although readily available, is ...

The mountain goat (Oreamnos americanus) is an iconic wildlife species of western North America that inhabits steep and largely inaccessible terrain in remote areas. They are at risk from human ...

An essential objective of software development is to locate and fix defects ahead of schedule that could be expected under diverse circumstances. Many software development activities are performed by ...