In this demo, we show how to use Rao-Blackwellised particle filtering to exploit the conditional independence structure of a simple DBN. The derivation and details are presented in A Simple Tutorial on Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks. This detailed discussion of the ABC network should complement the UAI2000 paper by Arnaud Doucet, Nando de Freitas, Kevin Murphy and Stuart Russell. After downloading the file, type "tar -xf demorbpfdbn.tar" to uncompress it. This creates the directory webalgorithm containing the required m files. Go to this directory, load matlab5 and type "dbnrbpf" for the demo.
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This code is described in "Computational Geometry in C" (Second Edition),
Chapter 8. It is not written to be comprehensible without the
explanation in that book.
Prints out one arm configuration to reach given target.
Assumes number of links >= 3.
Input:
nlinks Number of links
L1 L2 ... Ln Link lengths
x0 y0 target0
x1 x2 target1
看n2實(shí)例 #Create a simulator object
set ns [new Simulator]
#Define different colors for data flows
#$ns color 1 Blue
#$ns color 2 Red
#Open the nam trace file
set nf [open out-1.nam w]
$ns namtrace-all $nf
set f0 [open out0.tr w]
set f1 [open out1.tr w]
#Define a finish procedure
proc finish {} {
global ns nf
$ns flush-trace
#Close the trace file
close $nf
#Execute nam on the trace file
exit 0
}
#Create four nodes
set n0 [$ns node]
set n1 [$ns node]
set n2 [$ns node]
set n3 [$ns node]
#Create links between the nodes
$ns duplex-link $n0 $n2 1Mb 10ms
