I have a lack of understanding for the following task..
For given random variables X=(X_0,X_1,..X_M)
I have M samples x^i_0.
The Markov chain (MxM Matrix) describes the probability distribution for each of the random variable concerning the samples,that is
Two yachts, sailed by "Yacht 1" and "Yacht 2" respectively are sailing around a course. If the teams are even at the beginning of a lap then during that lap they embark on a duel and one team gains a 1 boat length advantage by the end of the lap. Otherwise the leading yacht always sails...
I have been worked out a transition matrix of a Markov chain using the Metropolis algorithm, and now have to use R in order to show that it has the required long
run distribution. I am new to R so im not sure if this is on the right track:
> A= matrix(c(.75, .6, .5, .1, .1, .1, .15, .3, .4)...
I'm trying to generate transition probabilities in R using the Markov Switching method.
I wrote the following code and then got the error as per following:
Error in if...
A very new European “Rapid Reaction Force for Fire” has been created today and begins operation between three Countries “A”, “B” and “C”. It’s main resource is a super aircraft “Funderbird2” with a massive water cannon that even carries a small mini-submarine for fighting fires at sea...
I have a problem with data fitting to gamma distribution. I have rainfall occurrences (calculated using 1st order 2 states Markov Chain analysis) for more than 30 yrs. I want to fit those data into Gamma Distribution and estimate parameters (Alpha and Beta). Then I want to simulate rainfall...
Hi anyone could help me with the following?
How do I calculate and interpret departure from non-randomness or likelihood of such configuration occurring using Exponential Random Graph Model with dependence graph construction if I have the following information:
- exponential probability...
The goal of this game is to get your opponents life points down to 0 before he gets yours down to 0. You take turns hitting each other until one persons health is at 0.
The variables are:
Attack (accuracy of hitting instead of missing)
Strength (how high you can hit)
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I want to make a prediction of the next state based on a training and test dataset. So I split my data into a train and test set and calculate the MLE on the train set and want to predict the next state on the test dataset.
The problem now is, that there can be...
I am trying to compute the hitting time of Markov chain. The way I formulated the problem is, what is the time to to hit state j (j in E) for the first time starting at state i (i in D). Any suggestion/help is appreciated.