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# MSAI Probability Exam Questions | ||
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1. | ||
- Sample spaces. | ||
- Union/intersection, complements to events, mutually exclusive events, implication, de Morgan's laws, partition of the sample space | ||
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2. | ||
- Naïve definition of probability. | ||
- Counting rules: multiplication rule, sampling with and without replacement, permutations and factorials. | ||
- The Birthday problem/paradox | ||
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3. | ||
- Binomial coefficients, Binomial theorem. | ||
- Bose-Einstein statistic, “stars and bars” | ||
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4. | ||
- Non-naïve definition of probability: probability spaces, properties of probability (additivity). | ||
- Frequentist and Bayesian view | ||
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5. | ||
- Inclusion-exclusion formula. | ||
- de Montmort’s matching problem | ||
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6. | ||
- Conditional probability: definition, prior/posterior. | ||
- Prosecutor’s fallacy. | ||
- Frequentist interpretation. | ||
- Martin Gardner's "Two children" puzzle | ||
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7. | ||
- Bayes' rule and the Law of Total Probability (LOTP). | ||
- Testing for diseases. | ||
- Conditional probabilities are probabilities | ||
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8. | ||
- Independence of events. | ||
- Independence of 3 events, pairwise independence. | ||
- Conditional independence | ||
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9. | ||
- Random variables. Indicator r.v.s. | ||
- Discrete and continuous r.v.s. | ||
- Distribution, probability mass function (PMF), its properties | ||
- Cumulative distribution function (CDF) | ||
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10. | ||
- Bernoulli and Binomial distributions | ||
- Hypergeometric distribution | ||
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11. | ||
- Functions of random variables. | ||
- Independence of random variables | ||
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12. | ||
- Expectation. | ||
- Linearity of expectation. | ||
- Expectation of Binomial and Hypergeometric distr-s | ||
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13. | ||
- Geometric and Negative Binomial distributions. | ||
- Coupon collector problem. | ||
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14. | ||
- Indicator r.v.s and the fundamental bridge. | ||
- Expectation in the card matching problem. | ||
- Boole, Bonferroni, inclusion-exclusion | ||
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15. | ||
- Law of the unconscious statistician (LOTUS). | ||
- St. Petersburg paradox | ||
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16. | ||
- Variance and standard deviation. | ||
- Variance of Binomial, Geometric, Negative Binomial distributions | ||
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17. | ||
- Continuous random variables. | ||
- Probability density function (PDF). | ||
- Expectation of a continuous r.v. | ||
- Logistic and Rayleigh distributions. | ||
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18. | ||
- Continuous uniform distribution, its expectation and variance. | ||
- Location-scale transformations, universality of uniform distribution | ||
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19. | ||
- The Normal distribution, its expectation and variance. Its symmetry. | ||
- Poisson integral (normalization of the normal). Standardized normal. | ||
- The 68-95-99 rule. | ||
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20. | ||
- Measures of central tendency: mean, median, mode. | ||
- What do they minimize. | ||
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21. | ||
- Moments. Their interpretation. | ||
- Skewness and Kurtosis. | ||
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22. | ||
- Sample moments. | ||
- Proof that sample std with 1/n is biased. | ||
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23. Joint, marginal, conditional distributions: discrete case. | ||
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24. | ||
- Joint, marginal, conditional distributions: continuous case. | ||
- Unit 2D circle distribution | ||
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25. | ||
- 2D LOTUS, expected distance between 2 uniforms. | ||
- Covariance and correlation, their properties | ||
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26. | ||
- Change of variables. | ||
- Log-normal distribution. | ||
- Chi-squared distribution | ||
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27. | ||
- Change of multiple variables, the Jacobian matrix. | ||
- Box-Muller method | ||
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28. | ||
- Convolutions. | ||
- Uniforms and exponentials convolutions | ||
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29. | ||
- Conditional expectation given an event. | ||
- Two-envelope paradox. | ||
- Time until HH vs HT | ||
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30. | ||
- Conditional expectation given an r.v. | ||
- Stick breaking. | ||
- Properties of conditional expectation. | ||
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31. | ||
- Cauchy-Schwarz inequality. | ||
- Second moment method | ||
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32. | ||
- Jensen’s inequality. | ||
- Relation to St. Petersburg paradox, bias of sample std. | ||
- Entropy, KL-divergence. | ||
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33. | ||
- Markov, Chebyshev, Chernoff inequalities. | ||
- Bounds on Normal tail probability | ||
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34. | ||
- Law of large numbers: weak and strong. | ||
- Running proportion of heads, Monte-Carlo idea | ||
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35. The Central Limit Theorem. | ||
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36. Chi-squared and Students’ t-distributions. | ||
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