Title:  Numerical Methods and Probability 

Code:  INM 

Ac.Year:  2017/2018 

Term:  Winter 

Curriculums:  

Language of Instruction:  Czech 

Credits:  5 

Completion:  credit+exam (written) 

Type of instruction:  Hour/sem  Lectures  Sem. Exercises  Lab. exercises  Comp. exercises  Other 

Hours:  26  13  0  13  0 

 Examination  Tests  Exercises  Laboratories  Other 

Points:  70  30  0  0  0 



Guarantor:  Novák Michal, RNDr., Ph.D., DMAT 

Lecturer:  Fuchs Petr, RNDr., Ph.D., DMAT Novák Michal, RNDr., Ph.D., DMAT 
Instructor:  Fuchs Petr, RNDr., Ph.D., DMAT Novák Michal, RNDr., Ph.D., DMAT Svoboda Zdeněk, RNDr., CSc., DMAT 

Faculty:  Faculty of Electrical Engineering and Communication BUT 

Department:  Department of Mathematics FEEC BUT 

Prerequisites:  

 Learning objectives: 

  In the first part the student will be acquainted with some numerical methods (approximation of functions, solution of nonlinear equations, approximate determination of a derivative and an integral, solution of differential equations) which are suitable for modelling various problems of practice. The other part of the subject yields fundamental knowledge from the probability theory (random event, probability, characteristics of random variables, probability distributions) which is necessary for simulation of random processes.  Description: 

  Numerical mathematics: Metric spaces, Banach theorem. Solution of nonlinear equations. Approximations of functions, interpolation, least squares method, splines. Numerical derivative and integral. Solution of ordinary differential equations, onestep and multistep methods. Probability: Random event and operations with events, definition of probability, independent events, total probability. Random variable, characteristics of a random variable. Probability distributions used, law of large numbers, limit theorems. Rudiments of statistical thinking.  Knowledge and skills required for the course: 

  Secondary school mathematics and some topics from Discrete Mathematics and Mathematical Analysis courses.  Learning outcomes and competences: 

  Students apply the gained knowledge in technical subjects when solving projects and writing the BSc Thesis. Numerical methods represent the fundamental element of investigation and practice in the present state of research.  Syllabus of lectures: 


 Introduction to numerical methods.
 Numerical solution of linear systems.
 Numerical solution of nonlinear equations and systems.
 Approximation, interpolation.
 Numercial integration and differentiation.
 ODE's: Introduction, numerical solution of firstorder initial value problems.
 Introduction to statistics, vizualization of statistical data.
 Introduction to probability theory, probability models, conditional and complete probability.
 Discrete and continuous random variables.
 Selected discrete distributions of probability.
 Selected continuous distributions of probability.
 Statistical testing.
 Reserve, revision, consultations.
 Syllabus of numerical exercises: 


 Classical and geometric probabilities.
 Discrete and continuous random variables.
 Expected value and dispersion.
 Binomial distribution.
 Poisson and exponential distributions.
 Uniform and normal distributions, ztest.
 Mean value test, power.
 Syllabus of computer exercises: 


 Nonlinear equation: Bisection method, regula falsi, iteration, Newton method.
 System of nonlinear equtations, interpolation.
 Splines, least squares method.
 Numerical differentiation and integration.
 Ordinary differential equations, analytical solution.
 Ordinary differential equations, analytical solution.
 Fundamental literature: 

  Ralston, A.: Základy numerické matematiky. Praha, Academia, 1978 (in Czech).
 Horová, I.: Numerické metody. Skriptum PřF MU Brno, 1999 (in Czech).
 Maroš, B., Marošová, M.: Základy numerické matematiky. Skriptum FSI VUT Brno, 1997 (in Czech).
 Loftus, J., Loftus, E.: Essence of Statistics. Second Edition, Alfred A. Knopf, New York 1988.
 Taha, H.A.: Operations Research. An Introduction. Fourth Edition, Macmillan Publishing Company, New York 1989.
 Montgomery, D.C., Runger, G.C.: Applied Statistics and Probability for Engineers. Third Edition. John Wiley & Sons, Inc., New York 2003
 Study literature: 


 Fajmon, B., Hlavičková, I., Novák, M., Vítovec, J.: Numerical Methods and Probability (Information technology), VUT v Brně, 2014
 Hlavičková, I., Hliněná, D.: Matematika 3. Sbírka úloh z pravděpodobnosti. VUT v Brně, 2015 (in Czech)
 Hlavičková, I., Novák, M.: Matematika 3 (zkrácená celoobrazovková verze učebního textu). VUT v Brně , 2014 (in Czech)
 Novák, M.: Matematika 3 (komentovaná zkoušková zadání pro kombinovanou formu studia). VUT v Brně, 2014 (in Czech)
 Novák, M.: Mathematics 3 (Numerical methods: Exercise Book), 2014
 Controlled instruction: 

  Ten written tests.  Progress assessment: 

   Ten 3point written tests: 30 points,
 final exam: 70 points.
Passing bounary for ECTS assessment: 50 points.
 Exam prerequisites: 

  To pass written tests with at least 10 points.  
