Czech

5

Not applicable.

Students completing this course should be able to:
In the field of probability and statistics:
- compute the basic characteristics of statistical data (mean, median, modus, variance, standard deviation)
- choose the correct probability model (classical, discrete, geometrical probability) for a given problem and compute the probability of a given event
- compute the conditional probability of a random event A given an event B
- recognize and use the independence of random events when computing probabilities
- apply the total probability rule and the Bayes' theorem
- work with the cumulative distribution function, the probability mass function of a discrete random variable and the probability density function of a continuous random variable
- construct the probability mass functions (in simple cases)
- choose the appropriate type of probability distribution in model cases (binomial, hypergeometric, exponential, etc.) and work with this distribution
- compute mean, variance and standard deviation of a random variable and explain the meaning of these characteristics
- perform computations with a normally distributed random variable X: find probability that X is in a given range or find the quantile/s for a given probability
- approximate the binomial distribution with help of the normal distribution
- perform simple hypothesis testing: Z-test, test on the mean of normal distribution variance known, test on the parameter p of the binomial distribution

In the field of numerical methods, the student should be able to:
- find the root of a given equation f(x)=0 using the bisection method, Newton method or the iterative method, describe these methods including the convergence conditions
- find the root of a system of two equations using Newton or iterative method
- solve a system of linear equations using Gaussian elimination with pivoting, Jacobi and Gauss-Seidel iteration methods, discuss the advantages and disadvantages of these methods
- find Lagrange or Newton interpolation polynomial for given points and use it for approximating the given function
- find the approximation of a function by spline functions
- find the approximation of a function given by table of points by the least squares method (linear, quadratic or exponential approximation)
- choose the most convenient type of approximation (interpolation polynomial, spline, least squares)
- estimate the derivative of a given function using numerical differentiation
- compute the numerical approximation of a definite integral using trapezoidal and Simpson method, describe the principal of these methods, compare them according to their accuracy
- find the approximate solution of a differential equation using Euler method, modified Euler methods and Runge-Kutta methods

The student should be able to apply the basic knowledge of combinatorics on the secondary school level: to explain the notions of variations, permutations and combinations, to determine their counts, to perform computations with factorials and binomial coefficients.
From the BMA1 and BMA2 courses, the basic knowledge of differential and integral calculus is demanded. Especially, the student should be able to sketch the graphs of elementary functions, to substitute into functions, to compute derivatives (including partial derivatives) and integrals.

Not applicable.

Teaching methods depend on the type of course unit as specified in the article 7 of BUT Rules for Studies and Examinations.

The final written test of maximum 70 points and maximum 30 points awarded during the semester.
During the semester, students have to hand in 5 homeworks (maximum 4 points per homework) and 2 computer laboratory tasks (maximum 5 points per task).
The final test consists of 7 tasks of equal point value (maximum 10 points each task). At least 3 tasks test knowledge of numerical methods and at least 3 tasks test knowledge of probability and statistics.

Five tutorials and two computer classes:

Tutorial 1: Introduction, Numerical methods I
Tutorial 2: Numerical methods II
Computer class 1: Introduction to Matlab, examples from numerical methods
Tutorial 3: Numerical methods completed, Probability and statistics I
Tutorial 4: Probability and statistics II
Computer class 2: Examples from probability and statistics
Tutorial 5: Repetition, seminar, information about the exam.

For detailed content of tutorials 1-4 cf. the topics of BMA3:

1. Introduction to descriptive statistics
2. Introduction to probability. Some probability models (classical, discrete, geometrical), conditional probability, dependence and independence of random events. Total probability rule and Bayes theorem.
3. Discrete random variables (probability mass function, cumulative distribution function, mean and variance).
4. Discrete probability distributions (binomial, geometric, hypergeometric, Poisson).
5. Continuous random variables (probability density function, distrubution function, mean, variance, quantiles). Exponencial distribution.
6. Normal distribution. Central limit theorem. Normal approximation to the binomial distribution.
7. Introduction to statistics. Z-test. Test of the mean of a normal distrinution, variance known.
8. Introduction to numerical methods. Numerical methods for root finding (bisection method, Newton method, iterative method)
9. Numerical solution of systems of nonlinear equations. Systems of linear equations (Gaussian elimination with pivoting, Jacobi and Gauss-Seidel iterative methods).
10. Interpolation: interpolation polynomial (Lagrange and Newton), splines (linear and cubic)
11. Least squares approximation. Numerical differentiation.
12. Numerical integration (trapezoidal and Simpson method).
13. Numerical solution of differential equations: initial problems (Euler method and its modifications, Runge-Kutta methods), boundary value problems (very briefly).

Not applicable.

The aim of this course is to introduce the basics of two mathematical disciplines: numerical methods, and probability and statistics.

The content and forms of instruction in the evaluated course are specified by a regulation issued by the lecturer responsible for the course (or by the coordinator of the combined form of study) and updated for every academic year.

Not applicable.

Not applicable.

Not applicable.

Not applicable.

12 hours, compulsory

Classes have the form of tutorials; BMA3 practical classes are as follows:

1. Classical and geometrical probability.
2. Discrete and continuous random variable.
3. Expected value and dispersion.
4. Binomial distribution. The sign test.
5. The Poisson and exponential distributions, queuing theory.
6. Uniform and normal distributions, binomial approximation of normal distribution, z-test.
(7. Mean expected value test, power.)