The probability theory of postgraduate entrance examination does not test the product formula, because the convolution formula is not the key to mastering the content.
I. Random events and probabilities
Examination content
The relationship between random events and sample space events and the basic properties of complete event group probability concept probability; Classical probability, geometric probability, basic formula of conditional probability; Independent repeated testing of events.
Second, random variables and their distribution
Examination content
Concept and Properties of Random Variables Distribution Function of Random Variables Probability Distribution of Discrete Random Variables Probability Density of Continuous Random Variables Distribution of Common Random Variables Distribution of Random Variable Functions.
Examination requirements
1, understand the concept and properties of distribution function, and calculate the probability of events related to random variables.
2. Understand the concept and probability distribution of discrete random variables, and master 0- 1 distribution, binomial distribution, geometric distribution, hypergeometric distribution, Poisson distribution and their applications.
3. Grasp the conclusion and application conditions of Poisson theorem, and use Poisson distribution to approximately represent binomial distribution.
4. Understand the concept of continuous random variables and their probability density, and master uniform distribution, normal distribution, exponential distribution and their applications.
5. Find the distribution of random variable function.