The educational materials on Bayesian decision making produced in AS Department are presented at this page. The material is organized so that to be potentially useful for different target groups of users: from students and PhD students to engineers solving practical problems. Textbooks are listed in the basic publications of the AS Department.

• AS and CSKI seminars
• others
  • NIPS workshop "Decision Making with Multiple Imperfect Decision Makers"

GPC toolbox serves for obtaining the basic knowledge about the Generalized Predictive Control (GPC). It is prepared for control experiments of Linear Single-Input Single-Output systems with Time-Invariant parameters (LTI SISO systems) described by Input Output differential equation or state-space form.

The GPC toolbox enables user to study the properties of the basic algorithm, generating full control actions and incremental predictive algorithm. The toolbox is prepared in two identical versions:

LQ toolbox was created out of the need to demonstrate the characteristics and application of

The library is designed using object oriented approach where decision-making is implemneted as a method of dedicated object: decision-maker. The library contains many commonly known decision-makers such as estimators and Bayesian filters. Support for control-oriented decision-makers (LQG control) is under development.

Controller tuning is a basic step in any control application. This tuning is a complex process composed of several steps starting with the plant analysis and ending with the verification of the designed controller. There exist various tools that help in particular steps of the design but the complete path of the design is not supported. This work makes an attempt to offer a procedure of "complete" controller design where all necessary steps follow automatically one after another. The idea is applied here to the LQG controller design.

Jobcontrol is a user friendly interface for Mixtools and Designer toolboxes. The Mixtools toolbox is a powerful set of utilities for system identification employing mixture models and the corresponding control design. It is implemented as set of M-scripts and MEX-binary exacutables for the Matlab computing environment. It suits to the goal of finding suitable structure for given data.

Mixtools is a toolbox designed for learning, prediction and control design with probability mixtures with a stress on fully probabilistic of strategies. The toolbox functions cover:

Application in Internal Medicine - Computer Image Diagnosing

Methods of medical image diagnosis are developed in AV CR project 1ET101050403. These methods are based on modelling of healthy and unhealthy tissue image pattern features using Gaussian mixture models. Decision making is based on Bayesian framework.

Internal medicine diseases are diagnosed.