Belépés címtáras azonosítással
magyar nyelvű adatlap
angol nyelvű adatlap
A tantárgy neve magyarul / Name of the subject in Hungarian: Valószínűségszámítás
Last updated: 2020. szeptember 5.
Computer Engineering BSc
Barbara Pintér associate
of Computer Science and Information Theory
Padmini Mukkamala lecturer Department of Computer Science and Information Theory
Elementary combinatory, calculus
A fenti forma a Neptun sajátja, ezen technikai okokból nem változtattunk.
A kötelező előtanulmányi rendek grafikus formában itt láthatók.
introduction. Basic concepts: random experiment, event space, event, elementary
event, operation between events, axioms, sigma algebra
of probability: Poincare-rule, Boole’s inequalities, continuity of probability
probability, independency of events, Theorem of total probability, Bayes’s
Theorem, produce theory
probability, geometrical probability. Examples for application: urn models,
variable, probability distribution function, discrete and continouos cases,
properties of the distribution function, probability of falling in an interval,
discrete distribution, probability density function
discrete random variables: binomial, Poisson, geometrical. Poisson
approximation to the binomial distribution. Memoryless properties of the
continouos distributions: uniform, exponential, normal. Simulation with uniform
distribution. Memoriless property of the exponential distribution. Standard
normal distribution. Linear transformation
value, deviation, moments. Theorems for expected value and
deviation. Expected value and deviation of notable dispersions.
Theorem, Markov’s- and Chebisev’s inequalities.
10. Joint distribution
function, projective distribution functions. Independency, convolution
(discrete and continouos cases), Joint density function, projective density
11. Theorems of large
numbers: Weak- and Stronge Law of Large Numbers. Central limit theorems,
correlation. Properties of covariance and correlations. Connection between
independency and uncorrelatedness
distribution, conditional expectation (regression). Linear regression.
Properties of regression. Examples of discrete and continouos cases.
14. Two-dimensional normal
distribution, polynomial distribution. Connection of the
independency and uncorrelatedness in normal case. Projections of the polinomial
distribution are binomials.
2 hours lectures and 2 hours
Period of study:
Participation in the exercises is
compulsory (TVSZ § 14 (3) in Code of studies and exams).
During the semester, a 120-point midterm test run will be written, at least 40 points are required for the
A 120-point final test run will
be written, at least 40 points are required to pass the exam. The results of
the semester interim test result and the examination result are included in the
test result at ratio 40% -60%.
total score = 0.4*min(Midterm;100) + 0.6*min(Final;100).
If the total score
is 40-54 points sufficient (2),
70-84 points good
In case of written exam’s result
at least sufficient, it is possible to change 1 mark upwards and downwards,
depending on the oral exam.
The repetition option for the
midterm test: the inadequate result can be improved or the missing result can
be replaced in a retake test. If the student has not written a midterm test,
writing the retake midterm test is compulsory. Without writing this he or she
will not get signature from the subject. If someone has failed the midterm
test, but does not participate in the substitution occasion, can try to get the
signature for a replacement fee in the first week of the exam period.
At the retake midterm test it is also possible to try to improve the
reached results in case of a successful midterm test’s result. The valid result
will be the greater one among the new result and 40.The new score will be valid
even if it is worse than the original. A valid result already cannot be
improved in the exam period.
During the semester at the lecturer's scheduled reception hours. We
organize consultations before midterm tests and exams.
Dr. László Ketskeméty
Department of Computer Science
and Information Theory