markov chain sequence 2

Hi,

I also need help with this question.

Suppose that the probability of snow on any day is conditional on whether or not it snowed on the preceding day. The probability that it will snow on a particular day given that it snowed on the day before is 0.65, and the probability that it will snow on a particular day given that it did not snow on the day before is 0.3. If the probability that it will snow on Friday is 0.6, what is the probability that it will snow on once on next 2 days?

so the answers is
is either snow and not snow + now snow and  snow.
Using a transition matrix how do I do this?
jagguyAsked:
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ozoCommented:
assuming that "on once" means "exactly once" an that "now snow" means "not snow"
let P(NS)=1-P(S)

P(S|Friday)*P(S|S)*P(NS|S)
+
P(S|Friday)*P(NS|S)*P(S|NS)
+
P(NS|Friday)*P(S|SN)*P(NS|S)
+
P(NS|Friday)*P(NS|NS)*P(S|NS)
0

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TommySzalapskiCommented:
If it snows on Friday, what are the probabilities for the next two days? (four possible outcomes)
If it does not snow on Friday what are the probabilities for the next two days? (four more possible outcomes)

Now you just need to adjust those final results by the probability that it snowed or did not snow on Friday.
0
jagguyAuthor Commented:
ok so what is the actual answer and the s0 matrix starts at
|1|
|0|
and then changes on each day via transition matrix?
0
TommySzalapskiCommented:
The initial matrix here would start as
|.6|
|.4|
0
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Math / Science

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