GVV: Offers. As you can see from our project page, there is a variety of very exciting projects on the boundary between computer vision and computer graphics that we are interested in.
We are always looking for students who are in pushing the limits of technology further within a Master or Bachelor thesis. We always have a variety of Master thesis topics available from the following areas of research. If you you want to get more detailed information, please get in touch with Prof. Christian Theobalt via Email. The group Graphics, Vision and Video and the Computer Graphics Group at MPI are looking for PhD students (wiss. SilverDecisions. Machine Learning & Statistical Learning. Weka 3 - Data Mining with Open Source Machine Learning Software in Java. Waikato Environment for Knowledge Analysis (WEKA) Decision Trees - Spreadsheet Analytics. Programs to Solve Decision Trees. ORMM Excel. Operations Research Models and Methods. Computation - Operations Research Models and Methods. The example on this page is from the field of stochastic programming.
Some of the parameters of a situation are originally not known with certainty, however, probability distributions for their values are given. Certain decisions must be made before the random variables are realized. After they are realized, other decisions may be made. The latter are called the recourse decisions, and this type of problem is called decision making with recourse.
We use the Jensen LP/IP add-in to solve the example, thus providing an illustration of the L-shaped method. This example was borrowed from An Optimization Primer, An Introduction to Linear, Nonlinear, Large Scale, Stochastic Programming and Variational Analysis, by Roger J-B Wets, January 11, 2005 (unpublished manuscript). Consider a product mix problem. Our problem is to select the product mix to maximize total profit, but the availability of the resources are not known. The mix chosen will require some number of hours for the resources. LP Addin Documentation. Decision Analysis Add-in. Math - What is "entropy and information gain"? Math - What is "entropy and information gain"? Decision Trees - Spreadsheet Analytics. Bkamins / SilverDecisions / wiki / Decision tree building walkthrough. As mentioned in the Introduction to decision trees, trees consist of a graph representing structure of a given sequential decision problem and numbers representing payoffs and probabilities.
When building the decision tree it makes life easier to first think of these elements separately, and only then analyse them in connection with one another. There are numerous books on decision analysis covering also the topic of decision trees, that a novice should have a look at [1, 2, 3]. By no means do we want to substitute for them, we would rather like to collect and summarize some general tips on building decision trees we find useful, and comment on how they link with how SilverDecisions works. Decision trees. Different types of decision trees. After reading an article in my newspaper about decision making and the role of regulators (dutch, source NRC) plus this older article (dutch, 1999, source nrc), I found two different types of decision trees.
First, there is one like Prof. Dr. Sweder van Wijnbergen developed for the dutch ministry of finance. Faculty.insead.edu/delquie/msp/Download Solutions/Expected Utility with TreePlan.pdf. Download free open source Matlab toolbox, matlab code, matlab source code. Expected utility hypothesis. In economics, game theory, and decision theory the expected utility hypothesis refers to a hypothesis concerning people's preferences with regard to choices that have uncertain outcomes (gambles).
This hypothesis states that if certain axioms are satisfied, the subjective value associated with a gamble by an individual is the statistical expectation of that individual's valuations of the outcomes of that gamble. This hypothesis has proved useful to explain some popular choices that seem to contradict the expected value criterion (which takes into account only the sizes of the payouts and the probabilities of occurrence), such as occur in the contexts of gambling and insurance. Daniel Bernoulli initiated this hypothesis in 1738. Until the mid twentieth century, the standard term for the expected utility was the moral expectation, contrasted with "mathematical expectation" for the expected value.[1] Expected value and choice under risk[edit] Bernoulli's formulation[edit] The St. Pomdps.pdf. Decision making under uncertainty. Intelligent agents acting in the world must be able to make complex decisions under uncertainty.
In artificial intelligence, solving the problem of deciding on a course of action is called planning. An example of an intelligent agent faced with a sequential decision making problem would be one that has to buy and sell stock on NASDAQ. This agent can observe the current price of stock and the amount being traded. It then has to estimate whether the stock’s value will increase or decrease. Given this estimate, the agent must decide on whether to buy or sell. The homepage of Finn Verner Jensen. Pages on "Bayesian Networks and Decision Graphs”" .
Link to web site for second edition: bndg.cs.aau.dk Pages on "An introduction to Bayesian Networks"" A connection of special interest to my home page is Hugin, which is the name of the Bayesian networks shell that I designed together with Stig Kjær Andersen and Kristian G. The homepage of Finn Verner Jensen. Decision-Trees.pdf. Decision tree. Traditionally, decision trees have been created manually.
A decision tree is a decision support tool that uses a tree-like graph or model of decisions and their possible consequences, including chance event outcomes, resource costs, and utility. It is one way to display an algorithm. Decision trees are commonly used in operations research, specifically in decision analysis, to help identify a strategy most likely to reach a goal. Overview[edit]
Download Trial TreePlan, SensIt, and SimVoi Add-Ins for Excel.