Model-Based Clustering, Classification, and Density Estimation Using mclust in R (Chapman & Hall/CRC The R Series) - Image 1

£55.18

CRC Press Model-Based Clustering, Classification, and Density Estimation Using mclust in R (Chapman & Hall/CRC The R Series)

Name: Model-Based Clustering, Classification, and Density Estimation Using mclust in R (Chapman & Hall/CRC The R Series)
Brand: CRC Press
Price: 55.18 GBP
Availability: InStock

Econometrics

This is the most expensive it has ever been. Walk away.

£55 today · previous high £55 · all-time low £40

NEW HERE?

Amazon shows you one price. We show you all of them.

Tosheroon watches Amazon prices so you don't have to. Every product on Amazon has a price history — we make it visible. Set the price you'd actually pay, and we'll email you the second it gets there. No app, no account, one email.

WHAT'S ON THIS PAGE

↓ Price chart
when this has been cheap or pricey

↓ Forecast
where the price is heading next

↓ Statistics
all-time high & low, recent range

↑ Price alert
name your number, we'll email you

Price History & Forecast

Grey patches = out of stock. Cheaper = lower on the chart. Hover for exact prices.

Last 616 days • 616 data points (No recent data available)

Historical

Generating forecast...

Price Distribution

Price distribution over 616 days • 5 price ranges

Days at Price

Current Price

Price Analysis

Most common range: £52-55 (185 days, 30.0%)

Price range: £40 - £55

Price levels: 5 price ranges over 616 days

Description

Model-based clustering and classification methods provide a systematic statistical approach to clustering, classification, and density estimation via mixture modeling. The model-based framework allows the problems of choosing or developing an appropriate clustering or classification method to be understood within the context of statistical modeling. The mclust package for the statistical environment R is a widely adopted platform implementing these model-based strategies. The package includes both summary and visual functionality, complementing procedures for estimating and choosing models. Key features of the book: An introduction to the model-based approach and the mclust R package A detailed description of mclust and the underlying modeling strategies An extensive set of examples, color plots, and figures along with the R code for reproducing them Supported by a companion website, including the R code to reproduce the examples and figures presented in the book, errata, and other supplementary material Model-Based Clustering, Classification, and Density Estimation Using mclust in R is accessible to quantitatively trained students and researchers with a basic understanding of statistical methods, including inference and computing. In addition to serving as a reference manual for mclust, the book will be particularly useful to those wishing to employ these model-based techniques in research or applications in statistics, data science, clinical research, social science, and many other disciplines.

Product Specifications

Brand: CRC Press
Format: paperback
ASIN: 1032234954
Category: Books > Subjects > Business, Finance & Law > Economics > Econometrics
Domain: Amazon UK
Release Date: 20 April 2023
Listed Since: 26 October 2022

Barcode

No barcode data available

CRC Press Model-Based Clustering, Classification, and Density Estimation Using mclust in R (Chapman & Hall/CRC The R Series)

This is the most expensive it has ever been. Walk away.

Price History & Forecast

Price Distribution

Price Analysis

Description

Product Specifications

Barcode

Similar Products You Might Like

Model-Based Clustering and Classification for Data Science: With Applications in R: Series Number 50 (Cambridge Series in Statistical and Probabilistic Mathematics, Series Number 50)

Handbook of Cluster Analysis (Chapman & Hall/CRC Handbooks of Modern Statistical Methods)

Handbook of Cluster Analysis (Chapman & Hall/CRC Handbooks of Modern Statistical Methods)

Cluster Analysis, 5th Edition (Wiley Series in Probability and Statistics)

Classification, Clustering, and Data Analysis: Recent Advances and Applications (Studies in Classification, Data Analysis, and Knowledge Organization)

Mixture Models: Parametric, Semiparametric, and New Directions (Chapman & Hall/CRC Monographs on Statistics and Applied Probability)

New Developments in Classification and Data Analysis: Proceedings of the Meeting of the Classification and Data Analysis Group (CLADAG) of the Italian ... Data Analysis, and Knowledge Organization)

Data Without Labels: Practical Unsupervised Machine Learning

Mixture Model-Based Classification

Reverse Clustering: Formulation, Interpretation and Case Studies: 957 (Studies in Computational Intelligence, 957)

Applied Biclustering Methods for Big and High-Dimensional Data Using R (Chapman & Hall/CRC Biostatistics Series)

Mathematical Classification and Clustering: 11 (Nonconvex Optimization and Its Applications, 11)

Data Analysis and Classification: Proceedings of the 6th Conference of the Classification and Data Analysis Group of the Società Italiana di ... Data Analysis, and Knowledge Organization)

Data Analysis, Classification, and Related Methods (Studies in Classification, Data Analysis, and Knowledge Organization)

Finite Mixture Models: 299 (Wiley Series in Probability and Statistics)

Multiblock Data Fusion in Statistics and Machine Learning: Applications in the Natural and Life Sciences

Multivariate Analysis (Wiley Series in Probability and Statistics)

Probability and Statistics for Computer Science

CRC Press Multiple Correspondence Analysis and Related Methods

Data Clustering in C++: An Object-Oriented Approach: 20 (Chapman & Hall/CRC Data Mining and Knowledge Discovery)

A First Course in Machine Learning (Chapman & Hall/CRC Machine Learning & Pattern Recognition)

Computational Business Analytics (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series)

Clustering: A Data Recovery Approach, Second Edition: 19 (Chapman & Hall/CRC Computer Science & Data Analysis)

Methods for Statistical Data Analysis of Multivariate Observations: 321 (Wiley Series in Probability and Statistics)