by another medium day, with a probability of 0.6, and equally like to be followed by a good or bad day. A bad day has a 0.3 probability of being followed by a good day, 0.2 of being followed by a medium day, and a 0.5 probability of being followed by another bad day. Construct a Markov chain model to describe the way the fishing days run. Referring to problem 1 in Chapter 2 (the fishing problem – reproduced below), calculate what you need to answer the following questions: If the fishing is medium on Monday, what is the probability that it will be medium on Thursday? If yesterday’s fishing was bad, what is the expected number of days of good fishing over the new week (7 days)? What percentage of days over the long run are good fishing days? If the fishing is bad today, what is the expected time (in days) until it is good? If the fishing is bad today, how long (in expected number of days) will it remain

Name: Commercial fishermen in Alaska go into the Bering Sea to catch all the
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Description: Commercial fishermen in Alaska go into the Bering Sea to catch all they can of a particular species (salmon, herring, etc.) during a restricted season of a few weeks. The schools of fish move about in a way that is very difficult to predict, so the f

Given:- Probabilities 0.5,0.3,0.2,0.6,0.5,0.3,0.2,and 0.5 To construct Markov chain model to…

Answered: by another medium day, with a probability of 0.6, and equally like to be followed by a good or bad day. A bad day has a 0.3 probability of being followed by a…

Advanced Engineering Mathematics

10th Edition

ISBN: 9780470458365

Author: Erwin Kreyszig

Publisher: Wiley, John & Sons, Incorporated

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Question

Commercial fishermen in Alaska go into the Bering Sea to catch all they can of a particular species (salmon, herring, etc.) during a restricted season of a few weeks. The schools of fish move about in a way that is very difficult to predict, so the fishing in a particular spot might be excellent one day and terrible the next. The day-to-day records of catch size were used to discover that the probability of a good fishing day being followed by another good day is 0.5, by a medium day 0.3, and by a poor day 0.2. A medium day is most likely to be followed by another medium day, with a probability of 0.6, and equally like to be followed by a good or bad day. A bad day has a 0.3 probability of being followed by a good day, 0.2 of being followed by a medium day, and a 0.5 probability of being followed by another bad day. Construct a Markov chain model to describe the way the fishing days run.

Referring to problem 1 in Chapter 2 (the fishing problem – reproduced below), calculate what you need to answer the following questions:

If the fishing is medium on Monday, what is the probability that it will be medium on Thursday?
If yesterday’s fishing was bad, what is the expected number of days of good fishing over the new week (7 days)?
What percentage of days over the long run are good fishing days?
If the fishing is bad today, what is the expected time (in days) until it is good?
If the fishing is bad today, how long (in expected number of days) will it remain bad before it gets better?

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