Author: Dianpeng Wang and V. Roshan Joseph
Description: This is a method (MinED) for mining probability distributions using deterministic sampling which is proposed by Joseph, Wang, Gu, Lv, and Tuo (2018). The MinED samples can be used for approximating the target distribution. They can be generated from a density function that is known only up to a proportionality constant and thus, it might find applications in Bayesian computation. Moreover, the MinED samples are generated with much fewer evaluations of the density function compared to random sampling-based methods such as MCMC and therefore, this method will be especially useful when the unnormalized posterior is expensive or time consuming to evaluate.
Packaged: 2018-12-05 08:22:40 UTC; Administrator

Description: This is a method (MinED) for mining probability distributions using deterministic sampling which is proposed by Joseph, Wang, Gu, Lv, and Tuo (2019) <DOI:10.1080/00401706.2018.1552203>. The MinED samples can be used for approximating the target distribution. They can be generated from a density function that is known only up to a proportionality constant and thus, it might find applications in Bayesian computation. Moreover, the MinED samples are generated with much fewer evaluations of the density function compared to random sampling-based methods such as MCMC and therefore, this method will be especially useful when the unnormalized posterior is expensive or time consuming to evaluate. This research is supported by a U.S. National Science Foundation grant DMS-1712642.

Title: minerva: Maximal Information-Based Nonparametric Exploration R
Authors@R: c(person("Michele", "Filosi", role = c("aut", "cre"),email =
"filosi@fbk.eu"), person("Roberto", "Visintainer", role =
"Albanese", role = "aut", email = "albanese@fbk.eu"),
person("Samantha", "Riccadonna", role = "ctb", email =
"riccadonna@fbk.eu"), person("Giuseppe", "Jurman", role =
Description: R wrapper for cmine implementation of Maximal
Information-based Nonparametric Exploration statistics (MIC and
Author: Michele Filosi [aut, cre], Roberto Visintainer [aut], Davide
Albanese [aut], Samantha Riccadonna [ctb], Giuseppe Jurman