ramidst (version 0.1.0)

map_inference_from_stream: Runs MAP inference from an AMIDST data stream

Description

Runs MAP inference from an AMIDST data stream

Usage

map_inference_from_stream(network, map_variables, evidence_variables, input_stream, sample_size, parallel = T, seed = 3L)

Arguments

network

a java object of class BayesianNetwork over which the computations will be carried out

map_variables

a vector with the name of the variables over which the MAP configuration will be computed

evidence_variables

a vector with the names of the observed variables

input_stream

and AMIDST data stream

sample_size

the sample size to be used for estimating marginals

parallel

a boolean indicating whether or not the items in the sample will be generated in parallel (when allowed by the system)

seed

the seed for the genertion of random numbers

Value

a data.frame with the MAP configuration of the variables of interest for each item in the stream

References

D. Ramos-Lopez, A. Salmeron, R. Rumi, A.M. Martinez, T.D. Nielsen, A.R. Masegosa, H. Langseth, A.L. Madsen (2016) Scalable MAP inference in Bayesian networks based on a Map-Reduce approach. PGM'2016. JMLR: Workshop and Conference Proceedings, vol. 52: 415-425.

Examples

## Not run: 
# network <- load_amidst_bn(system.file("extdata","WasteIncinerator.bn",
# package="ramidst"))
# sample_stream <- amidst_data_stream(system.file("extdata",
# "WasteIncineratorSample.arff",package="ramidst"))
# map_configurations <- map_inference_from_stream(network,c("D","B"),c("W"),
# sample_stream,5L)
# map_configurations
# ## End(Not run)