The purpose of this paper is to provide a practical introduction to the discrete Kalman
filter. This introduction includes a description and some discussion of the basic
discrete Kalman filter, a derivation, description and some discussion of the extended
Kalman filter, and a relatively simple (tangible) example with real numbers &
results.
The present paper deals with the problem of calculating mean delays in polling systems
with either exhaustive or gated service. We develop a mean value analysis (MVA) to
compute these delay figures. The merits of MVA are in its intrinsic simplicity and its
intuitively appealing derivation. As a consequence, MVA may be applied, both in an
exact and approximate manner, to a large variety of models.
n 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.
On-Line MCMC Bayesian Model Selection
This demo demonstrates how to use the sequential Monte Carlo algorithm with reversible jump MCMC steps to perform model selection in neural networks. We treat both the model dimension (number of neurons) and model parameters as unknowns. The derivation and details are presented in: Christophe Andrieu, Nando de Freitas and Arnaud Doucet. Sequential Bayesian Estimation and Model Selection Applied to Neural Networks . Technical report CUED/F-INFENG/TR 341, Cambridge University Department of Engineering, June 1999. After downloading the file, type "tar -xf version2.tar" to uncompress it. This creates the directory version2 containing the required m files. Go to this directory, load matlab5 and type "smcdemo1". In the header of the demo file, one can select to monitor the simulation progress (with par.doPlot=1) and modify the simulation parameters.
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.
This demo nstrates how to use the sequential Monte Carlo algorithm with reversible jump MCMC steps to perform model selection in neural networks. We treat both the model dimension (number of neurons) and model parameters as unknowns. The derivation and details are presented in: Christophe Andrieu, Nando de Freitas and Arnaud Doucet. Sequential Bayesian Estimation and Model Selection Applied to Neural Networks . Technical report CUED/F-INFENG/TR 341, Cambridge University Department of Engineering, June 1999. After downloading the file, type "tar -xf version2.tar" to uncompress it. This creates the directory version2 containing the required m files. Go to this directory, load matlab5 and type "smcdemo1". In the header of the demo file, one can select to monitor the simulation progress (with par.doPlot=1) and modify the simulation parameters.
The algorithms are coded in a way that makes it trivial to apply them to other problems. Several generic routines for resampling are provided. The derivation and details are presented in: Rudolph van der Merwe, Arnaud Doucet, Nando de Freitas and Eric Wan. The Unscented Particle Filter. Technical report CUED/F-INFENG/TR 380, Cambridge University Department of Engineering, May 2000. After downloading the file, type "tar -xf upf_demos.tar" to uncompress it. This creates the directory webalgorithm containing the required m files. Go to this directory, load matlab5 and type "demo_MC" for the demo.
Matlab 畫三維立體圖形
The aim of geom3d library is to handle and visualize 3D geometric primitives
such as points, lines, planes, polyhedra... It provides low-level functions
for manipulating 3D geometric primitives, making easier the development of more
complex geometric algorithms.
Some features of the library are:
- creation of various shapes (3D points, 3D lines, planes, polyhedra...)
through an intuitive syntax.
Ex: createPlane(p1, p2, p3) to create a plane through 3 points.
- derivation of new shapes: intersection between 2 planes, intersection between
a plane and a line, between a sphere and a line...
- functions for 3D polygons and polyhedra. Polyhedra use classical vertex-faces
arrays (face array contain indices of vertices), and support faces with any
number of vertices. Some basic models are provided (createOctaedron,
createCubeoctaedron...), as well as some computation (like faceNormal or
centroid)
- manipulation of planar transformation. Ex.:
ROT = createRotationOx(THETA);
P2 = transformPoint3d(P1, ROT);
- direct drawing of shapes with specialized functions. Clipping is performed
automatically for infinite shapes such as lines or rays. Ex:
drawPoint3d([50 50 25; 20 70 10], 'ro'); % draw some points
drawLine3d([X0 Y0 Z0 DX DY DZ]); % clip and draw straight line
Some functions require the geom2d package.
Additional help is provided in geom3d/Contents.m file, as well as summary files
like 'points3d.m' or 'lines3d.m'.
