marks

Examination marks of 88 students on five different
  topics, from Mardia (1979).

datasets

Bayesian network structure learning via constraint-based
        (also known as 'conditional independence'), score-based and
        hybrid algorithms. This package implements the Grow-Shrink (GS)
        algorithm, the Incremental Association (IAMB) algorithm, the
        Interleaved-IAMB (Inter-IAMB) algorithm, the Fast-IAMB
        (Fast-IAMB) algorithm, the Max-Min Parents and Children (MMPC)
        algorithm, the Hill-Climbing (HC) greedy search algorithm, the
        Max-Min Hill-Climbing (MMHC) algorithm for both discrete and
        Gaussian networks, along with many score functions and
        conditional independence tests. Some utility functions (model
        comparison and manipulation, random data generation, arc
        orientation testing, simple and advanced plots) are included,
        as well as basic parametric and bootstrap inference functions.

Marco Scutari

bnlearn

Bayesian network structure learning

marks function

The <code>marks</code> data set contains the following variables,
 one for each topic in the examination:
 <ul><li><code>MECH</code>(mechanics)</li><li><code>VECT</code>(vectors)</li><li><code>ALG</code>(algebra)</li><li><code>ANL</code>(analysis)</li><li><code>STAT</code>(statistics)</li></ul> All are measured on the same scale (0-100).

format

Edwards DI (2000). Introduction to Graphical Modelling.
 Springer, 2nd edition. Mardia KV, Kent JT, Bibby JM (1979). Multivariate Analysis.
 Academic Press. Whittaker J (1990). Graphical Models in Applied Multivariate
 Statistics. Wiley.

marks: Examination marks data set

Description

Usage

Arguments

format

source

Examples