贝叶斯网络-学习笔记bnlearn

贝叶斯网络

library(bnlearn)
dag<-empty.graph(nodes=c('A','S','E','O','R','T'))
arcs.set<-matrix(c("A", "E", "S", "E", "E", "O", "E", "R", "O", "T", "R", "T"), byrow = TRUE, ncol = 2, dimnames = list(NULL, c("from", "to")))
arcs(dag)<-arcs.set
dag

  Random/Generated Bayesian network

  model:
   [A][S][E|A:S][O|E][R|E][T|O:R] 
  nodes:                                 6 
  arcs:                                  6 
    undirected arcs:                     0 
    directed arcs:                       6 
  average markov blanket size:           2.67 
  average neighbourhood size:            2.00 
  average branching factor:              1.00 

  generation algorithm:                  Empty 

arcs(dag)

     from to 
[1,] "A"  "E"
[2,] "S"  "E"
[3,] "E"  "O"
[4,] "E"  "R"
[5,] "O"  "T"
[6,] "R"  "T"

modelstring(dag)

[1] "[A][S][E|A:S][O|E][R|E][T|O:R]"

learned<-hc(alarm)
learned

  Bayesian network learned via Score-based methods

  model:
   [HIST][HRBP][PAP][FIO2][ANES][ERCA][LVF|HIST][PMB|PAP][ERLO|HRBP][PCWP|LVF]
   [HR|HRBP:ERLO][HREK|HR:ERCA][HRSA|HR:ERCA][LVV|PCWP:LVF][CCHL|HR][CVP|LVV]
   [MINV|CCHL][STKV|LVF:LVV][CO|STKV:HR][HYP|LVV:STKV][VALV|MINV][INT|MINV:VALV]
   [PVS|FIO2:VALV][ACO2|CCHL:VALV][PRSS|INT:VALV][SHNT|PMB:INT]
   [VLNG|MINV:INT:VALV][SAO2|SHNT:PVS][ECO2|ACO2:VLNG][KINK|PRSS:VLNG]
   [VTUB|PRSS:MINV:INT][TPR|SAO2:CCHL][DISC|VTUB][BP|TPR:CO][APL|TPR]
   [VMCH|DISC:VTUB][MVS|VMCH]
  nodes:                                 37 
  arcs:                                  53 
    undirected arcs:                     0 
    directed arcs:                       53 
  average markov blanket size:           3.46 
  average neighbourhood size:            2.86 
  average branching factor:              1.43 

  learning algorithm:                    Hill-Climbing 
  score:                                 BIC (disc.) 
  penalization coefficient:              4.951744 
  tests used in the learning procedure:  2718 
  optimized:                             TRUE 

graphviz.plot(learned)

Loading required namespace: Rgraphviz

dag.bnlearn <- model2network("[G][E][V|G:E][N|V][W|V][C|N:W]")
dsep(dag.bnlearn,'G','V')

[1] FALSE

graphviz.plot(dag.bnlearn)

dsep(dag.bnlearn, "E", "V", "V")

[1] TRUE