Applied Directional Statistics Applied Directional Statistics
Chapman & Hall/CRC Interdisciplinary Statistics

Applied Directional Statistics

Modern Methods and Case Studies

    • 67,99 $US
    • 67,99 $US

Description de l’éditeur

This book collects important advances in methodology and data analysis for directional statistics. It is the companion book of the more theoretical treatment presented in Modern Directional Statistics (CRC Press, 2017). The field of directional statistics has received a lot of attention due to demands from disciplines such as life sciences or machine learning, the availability of massive data sets requiring adapted statistical techniques, and technological advances. This book covers important progress in bioinformatics, biology, astrophysics, oceanography, environmental sciences, earth sciences, machine learning and social sciences.

GENRE
Science et nature
SORTIE
2018
3 septembre
LANGUE
EN
Anglais
LONGUEUR
318
Pages
ÉDITIONS
CRC Press
VENDEUR
Taylor & Francis Group
TAILLE
13
Mo
Directional Statistics for Innovative Applications Directional Statistics for Innovative Applications
2022
Case Studies in Spatial Point Process Modeling Case Studies in Spatial Point Process Modeling
2006
Statistics for Spatial Data Statistics for Spatial Data
2015
Innovations in Multivariate Statistical Modeling Innovations in Multivariate Statistical Modeling
2022
Advances in Directional and Linear Statistics Advances in Directional and Linear Statistics
2010
Multiscale Modeling Multiscale Modeling
2007
Bayesian Analysis of Capture-Recapture Data with Hidden Markov Models Bayesian Analysis of Capture-Recapture Data with Hidden Markov Models
2026
Statistics for Fission Track Analysis Statistics for Fission Track Analysis
2005
Statistical and Computational Pharmacogenomics Statistical and Computational Pharmacogenomics
2008
Time Series Modeling of Neuroscience Data Time Series Modeling of Neuroscience Data
2012
Markov Chain Monte Carlo in Practice Markov Chain Monte Carlo in Practice
1995
Meta-analysis of Binary Data Using Profile Likelihood Meta-analysis of Binary Data Using Profile Likelihood
2008