1. Tossing a Die Successive tossing of a die can be modeled as a stochastic process: X1, X2, : (2, P) → {1, 2, 3, 4, 5, 6}. ● .... We assume that the random variables (Xt) teN are identically and independently distributed (iid). Problems to be solved in your lab session: P.1: Explain in your own words why the assumption that all Xt are iid is justified in the case of tossing a die. P.2: Give a real-world example of a stochastic process, where it is not justified to claim that the random variables are iid.

A First Course in Probability (10th Edition)
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ISBN:9780134753119
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Chapter1: Combinatorial Analysis
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1. Tossing a Die
Successive tossing of a die can be modeled as a stochastic process:
X1, X2,
: (S2, P) → {1, 2, 3, 4, 5, 6}.
We assume that the random variables (Xt) teN are identically and independently distributed
(iid).
Problems to be solved in your lab session:
www
HIDH
P.1: Explain in your own words why the assumption that all X+ are iid is
justified in the case of tossing a die.
P.2: Give a real-world example of a stochastic process, where it is not justified to
claim that the random variables are iid.
Transcribed Image Text:1. Tossing a Die Successive tossing of a die can be modeled as a stochastic process: X1, X2, : (S2, P) → {1, 2, 3, 4, 5, 6}. We assume that the random variables (Xt) teN are identically and independently distributed (iid). Problems to be solved in your lab session: www HIDH P.1: Explain in your own words why the assumption that all X+ are iid is justified in the case of tossing a die. P.2: Give a real-world example of a stochastic process, where it is not justified to claim that the random variables are iid.
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