Class BayesianNetwork

constructor

• new BayesianNetwork(alpha: number): BayesianNetwork

• Parameters

  • alpha: number

    Equivalent sample size

  Returns BayesianNetwork

_alpha

_cand

_ess

_graph

_n

_score_method

_th

_bdeu

• _bdeu(x: any, graph?: any[], cand?: any[], exact?: boolean): number

• Parameters

  • x: any
  • Optionalgraph: any[]
  • Optionalcand: any[]
  • Optionalexact: boolean

  Returns number

_count

• _count(x: any, graph?: any[], cand?: any[]): ArrayKeyMap[]

• Parameters

  • x: any
  • Optionalgraph: any[]
  • Optionalcand: any[]

  Returns ArrayKeyMap[]

_fitParameter

• _fitParameter(x: any): void

• Parameters

  • x: any

  Returns void

_fitStructure

• _fitStructure(x: any): void

• Parameters

  • x: any

  Returns void

_fitStructure_dp

• _fitStructure_dp(x: any): void

• Parameters

  • x: any

  Returns void

_logBDeu_appro

• _logBDeu_appro(x: any, graph?: any[], cand?: any[]): number

• Parameters

  • x: any
  • Optionalgraph: any[]
  • Optionalcand: any[]

  Returns number

_logBDeu_exact

• _logBDeu_exact(x: any, graph?: any[], cand?: any[]): number

• Parameters

  • x: any
  • Optionalgraph: any[]
  • Optionalcand: any[]

  Returns number

_score

• _score(x: any, graph?: any[], cand?: any[]): number

• Parameters

  • x: any
  • Optionalgraph: any[]
  • Optionalcand: any[]

  Returns number

fit

• fit(x: any[][]): void

• Fit model.

  Parameters

  • x: any[][]

    Training data

  Returns void

probability

• probability(x: any[][]): number[]

• Returns probability values.

  Parameters

  • x: any[][]

    Sample data

  Returns number[]

  Predicted values