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exam questions list
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# MSAI Probability Exam Questions

1.
- Sample spaces.
- Union/intersection, complements to events, mutually exclusive events, implication, de Morgan's laws, partition of the sample space

2.
- Naïve definition of probability.
- Counting rules: multiplication rule, sampling with and without replacement, permutations and factorials.
- The Birthday problem/paradox

3.
- Binomial coefficients, Binomial theorem.
- Bose-Einstein statistic, “stars and bars”

4.
- Non-naïve definition of probability: probability spaces, properties of probability (additivity).
- Frequentist and Bayesian view

5.
- Inclusion-exclusion formula.
- de Montmort’s matching problem

6.
- Conditional probability: definition, prior/posterior.
- Prosecutor’s fallacy.
- Frequentist interpretation.
- Martin Gardner's "Two children" puzzle

7.
- Bayes' rule and the Law of Total Probability (LOTP).
- Testing for diseases.
- Conditional probabilities are probabilities

8.
- Independence of events.
- Independence of 3 events, pairwise independence.
- Conditional independence

9.
- Random variables. Indicator r.v.s.
- Discrete and continuous r.v.s.
- Distribution, probability mass function (PMF), its properties
- Cumulative distribution function (CDF)

10.
- Bernoulli and Binomial distributions
- Hypergeometric distribution

11.
- Functions of random variables.
- Independence of random variables

12.
- Expectation.
- Linearity of expectation.
- Expectation of Binomial and Hypergeometric distr-s

13.
- Geometric and Negative Binomial distributions.
- Coupon collector problem.

14.
- Indicator r.v.s and the fundamental bridge.
- Expectation in the card matching problem.
- Boole, Bonferroni, inclusion-exclusion

15.
- Law of the unconscious statistician (LOTUS).
- St. Petersburg paradox

16.
- Variance and standard deviation.
- Variance of Binomial, Geometric, Negative Binomial distributions

17.
- Continuous random variables.
- Probability density function (PDF).
- Expectation of a continuous r.v.
- Logistic and Rayleigh distributions.

18.
- Continuous uniform distribution, its expectation and variance.
- Location-scale transformations, universality of uniform distribution

19.
- The Normal distribution, its expectation and variance. Its symmetry.
- Poisson integral (normalization of the normal). Standardized normal.
- The 68-95-99 rule.

20.
- Measures of central tendency: mean, median, mode.
- What do they minimize.

21.
- Moments. Their interpretation.
- Skewness and Kurtosis.

22.
- Sample moments.
- Proof that sample std with 1/n is biased.

23. Joint, marginal, conditional distributions: discrete case.

24.
- Joint, marginal, conditional distributions: continuous case.
- Unit 2D circle distribution

25.
- 2D LOTUS, expected distance between 2 uniforms.
- Covariance and correlation, their properties

26.
- Change of variables.
- Log-normal distribution.
- Chi-squared distribution

27.
- Change of multiple variables, the Jacobian matrix.
- Box-Muller method

28.
- Convolutions.
- Uniforms and exponentials convolutions

29.
- Conditional expectation given an event.
- Two-envelope paradox.
- Time until HH vs HT

30.
- Conditional expectation given an r.v.
- Stick breaking.
- Properties of conditional expectation.

31.
- Cauchy-Schwarz inequality.
- Second moment method

32.
- Jensen’s inequality.
- Relation to St. Petersburg paradox, bias of sample std.
- Entropy, KL-divergence.

33.
- Markov, Chebyshev, Chernoff inequalities.
- Bounds on Normal tail probability

34.
- Law of large numbers: weak and strong.
- Running proportion of heads, Monte-Carlo idea

35. The Central Limit Theorem.

36. Chi-squared and Students’ t-distributions.

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