A grasshopper hops between three flowers labeled 1, 2, 3. It starts from flower 1. Once it arrives at a flower, it sits there for a period of time distributed exponentially with parameter 2. Then it makes a jump, as follows: if it is located on flower 1, it jumps to 2 or 3 with equal probability; if it is located at 2, it jumps to 3, and if it is at 3, it jumps to 2. (a) Model the grasshopper using a continuous-time Markov chain. Find the states, the initial distribution, and the jump rates! (b) What is the probability that at time t the grasshopper will sit on the third flower?
Continuous Probability Distributions
Probability distributions are of two types, which are continuous probability distributions and discrete probability distributions. A continuous probability distribution contains an infinite number of values. For example, if time is infinite: you could count from 0 to a trillion seconds, billion seconds, so on indefinitely. A discrete probability distribution consists of only a countable set of possible values.
Normal Distribution
Suppose we had to design a bathroom weighing scale, how would we decide what should be the range of the weighing machine? Would we take the highest recorded human weight in history and use that as the upper limit for our weighing scale? This may not be a great idea as the sensitivity of the scale would get reduced if the range is too large. At the same time, if we keep the upper limit too low, it may not be usable for a large percentage of the population!
A grasshopper hops between three flowers labeled 1, 2, 3. It starts from flower 1. Once it arrives at a flower, it sits there for a period of time distributed exponentially with parameter 2. Then it makes a jump, as follows: if it is located on flower 1, it jumps to 2 or 3 with equal
(a) Model the grasshopper using a continuous-time Markov chain. Find the states, the initial distribution, and the jump rates!
(b) What is the probability that at time t the grasshopper will sit on the third flower?
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