Applied Mathematics

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Department of Applied Mathematics

Synopsis:

Descriptive statistics. Probability, conditional probability, Bayes theorem. Random variable, random vector, joint and marginal probability distribution, independence. Some discrete and continuous distributions. Mixed distributions. Finite mixture of distributions. Point estimation. Interval estimation. Hypothesis testing. Regression and correlation analysis. Simple graphs, multigraphs, labeled graph, planar graph, Euler´s formula. Four-color problem. Euler Circuit, Hamilton circuit and directed graphs. Algorithms for finding distances in digraphs - Dijkstra´s algorithm. Weighted graphs, shortest paths and minimal spanning trees. A depth first spanning tree, a breadth first spanning tree. Adjacency matrices. Flows in networks, Ford-Fulkerson´s algorithm.

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No time-table has been prepared for this course

The course is a part of the following study plans: