Bokus

T. Brendan Murphy – författare

Visar alla böcker från författaren T. Brendan Murphy. Handla med fri frakt och snabb leverans.

6 produkter

  1. Adrian E. Raftery, T. Brendan Murphy, Chris Fraley, Luca Scrucca - Model-Based Clustering, Classification, and Density Estimation Using mclust in R, E-bok. Tillgänglighet: Lägg i varukorg

    Model-Based Clustering, Classification, and Density Estimation Using mclust in R

    Av Adrian E. Raftery, T. Brendan Murphy m. fl.

    E-bok, 2023

    1051 kr

    Lägg i varukorg

    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 …

  2. Adrian E. Raftery, T. Brendan Murphy, Chris Fraley, Luca Scrucca - Model-Based Clustering, Classification, and Density Estimation Using mclust in R, E-bok. Tillgänglighet: Lägg i varukorg

    Model-Based Clustering, Classification, and Density Estimation Using mclust in R

    Av Adrian E. Raftery, T. Brendan Murphy m. fl.

    E-bok, 2023

    1074 kr

    Lägg i varukorg

    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 …

  3. Luca Scrucca, Chris Fraley, T. Brendan Murphy, Adrian E. Raftery - Model-Based Clustering, Classification, and Density Estimation Using mclust in R, Häftad. Tillgänglighet: Lägg i varukorg

    Model-Based Clustering, Classification, and Density Estimation Using mclust in R

    Av Luca Scrucca, Chris Fraley m. fl.

    Häftad, 2023

    910 kr

    Lägg i varukorg

    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 …

  4. Luca Scrucca, Chris Fraley, T. Brendan Murphy, Adrian E. Raftery - Model-Based Clustering, Classification, and Density Estimation Using mclust in R, Inbunden. Tillgänglighet: Lägg i varukorg

    Model-Based Clustering, Classification, and Density Estimation Using mclust in R

    Av Luca Scrucca, Chris Fraley m. fl.

    Inbunden, 2023

    2513 kr

    Lägg i varukorg

    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 …

  5. Charles Bouveyron, Gilles Celeux, T. Brendan Murphy, Adrian E. Raftery - Model-Based Clustering and Classification for Data Science, Inbunden. Tillgänglighet: Lägg i varukorg

    Model-Based Clustering and Classification for Data Science

    With Applications in R

    Av Charles Bouveyron, Gilles Celeux m. fl.

    Inbunden, 2019

    1214 kr

    Lägg i varukorg

    Cluster analysis finds groups in data automatically. Most methods have been heuristic and leave open such central questions as: how many clusters are there? Which method should I use? How should I handle outliers? Classification assigns new observations to groups given previously classified …

  6. Adrian E. Raftery, T. Brendan Murphy, Gilles Celeux, Charles Bouveyron - Model-Based Clustering and Classification for Data Science, E-bok. Tillgänglighet: Lägg i varukorg

    Model-Based Clustering and Classification for Data Science

    With Applications in R

    Av Adrian E. Raftery, T. Brendan Murphy m. fl.

    E-bok, 2019

    1265 kr

    Lägg i varukorg

    Cluster analysis finds groups in data automatically. Most methods have been heuristic and leave open such central questions as: how many clusters are there? Which method should I use? How should I handle outliers? Classification assigns new observations to groups given previously classified …