Where is the probability test of postgraduate mathematics?

Random events and probability, random variables and their distribution, two-dimensional random variables and their distribution, numerical characteristics of random variables, law of large numbers and central limit theorem, basic concepts of mathematical statistics, parameter estimation and hypothesis testing. All the knowledge involved in probability theory and mathematical statistics.

1, exchange law, association law, distribution rate, Morgan's law; (the basis of solving problems)

2. Classical probability-finite equal possibility, geometric model-infinite equal possibility;

3, the principle of drawing lots-has nothing to do with the order;

4, the principle of small probability-small probability events can't happen in a test, once it happens, it will doubt the correctness of the realization law;

5. Conditional probability: Note that the probability of a condition must be greater than 0;

6. Overview: Cause > Result Bayesian: Result >; Reason;

7. Compatibility is defined by events and independence by probability.

chapter two

The values of 1, 0- 1 distribution, binomial distribution and Poisson distribution all start from 0;

2. The distribution function is right continuous, and the distribution function should be written as right continuous as far as possible;

3. Properties of distribution function and probability density;

4. The probability of any specified value of continuous random variable is 0;

5. A probability of 0 is not necessarily an impossible event, and a probability of 1 is not necessarily an inevitable event;

6. The graphic characteristics of normal distribution;

7. Try to find the distribution of the function according to the definition method, and write the basic formula according to the definition;

8. When the segmentation is monotonous, the formula should be used in segments and then added.