predictBN: Downscale with Bayesian Networks

In MNLR/BNWeatherGen: Weather Generators with Bayesian Networks

Downscale with Bayesian Networks

Description

Downscale with Bayesian Networks

Usage

predictBN(
  cbn,
  x,
  y = NULL,
  output = "probabilities",
  prediction.type = "exact",
  event = "1",
  threshold.vector = NULL,
  output.attr.evidence = FALSE,
  cl = NULL,
  stop.cluster = TRUE,
  parallelize = FALSE,
  n.cores = NULL,
  cluster.type = "FORK"
)

Arguments

cbn	Climatic Bayesian Network, as returned by either buildCBN() or buildDynamicCBN().
x	may be output of prepareNewData for predictive non-past, NULL for a no-G dynamic CBN or a single dataset for a G dynamic CBN (i.e. dynamic CBN do not accept members).
y	Predictands
output	Options are: "probabilities", "event" and "probabilities.list". You should probably not use the last one. Note that if prediction.type = "simulation", output is forced to "event".
prediction.type	Options are "exact", "approximate" and "simulation". Exact inference requires a compilable junction.

Author(s)

M.N. Legasa

MNLR/BNWeatherGen documentation built on June 2, 2023, 9:02 p.m.