This preview shows pages 1–4. Sign up to view the full content.
This preview has intentionally blurred sections. Sign up to view the full version.
View Full DocumentThis preview has intentionally blurred sections. Sign up to view the full version.
View Full Document
Unformatted text preview: 1 A , X ( 1 ) = 1 , and X ( 2 ) = 2 . For an arbitrary , what possible values can X ( ) take? Justify your answer fully. b. Given the information in part a and that P ( A ) = 0 . 2 , nd the distribution of X (or characterize it in terms of a CDF, PDF, or PMF). 3 4. (10 pts.) Consider a discrete real random variable X , with probability generating function G X . a. Use the Markov inequality to derive an upper bound involving G X , analogous to the Chernoff bound, for the tail probability P { X a } , a R . b. Apply part a to obtain a tailprobability bound for the Poisson distribution with parameter , assuming a . 4...
View Full
Document
 Spring '08
 Staff

Click to edit the document details