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What does this Markov Chain notation mean?
Discrete Markov chainMarkov chain basic positive recurrency questionMarkov chain, enter timeWhat does this question about classifying the states of this Markov chain mean?This is a Markov Chain?Markov Process, Markov ChainGiven a stochastic matrix $P$, find a markov chain ${X_n}$ having $P$ as its transition matrixHow to construct a Markov Chain of higher order given another Markov ChainDiscretize a continuous time Markov ChainSimulation of Markov chain
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So, I don't know the meaning of much of the notation present in the following slide.
I know basically what a Discrete Markov Chain and a Transitional Probability Matrix are, but I'd like to what exactly those notations are saying.
stochastic-processes notation markov-chains markov-process
asked Mar 20 at 0:03
Fabrício SantanaFabrício Santana
183
$endgroup$
add a comment |
$begingroup$
So, I don't know the meaning of much of the notation present in the following slide.
I know basically what a Discrete Markov Chain and a Transitional Probability Matrix are, but I'd like to what exactly those notations are saying.
stochastic-processes notation markov-chains markov-process
asked Mar 20 at 0:03
Fabrício SantanaFabrício Santana
183
$endgroup$
1
$begingroup$
Do you know what conditional probability is?
$endgroup$
– David M.
Mar 20 at 0:11
add a comment |
$begingroup$
So, I don't know the meaning of much of the notation present in the following slide.
I know basically what a Discrete Markov Chain and a Transitional Probability Matrix are, but I'd like to what exactly those notations are saying.
stochastic-processes notation markov-chains markov-process
asked Mar 20 at 0:03
Fabrício SantanaFabrício Santana
183
$endgroup$
So, I don't know the meaning of much of the notation present in the following slide.
I know basically what a Discrete Markov Chain and a Transitional Probability Matrix are, but I'd like to what exactly those notations are saying.
stochastic-processes notation markov-chains markov-process
stochastic-processes notation markov-chains markov-process
asked Mar 20 at 0:03
Fabrício SantanaFabrício Santana
183
asked Mar 20 at 0:03
Fabrício SantanaFabrício Santana
183
asked Mar 20 at 0:03
Fabrício SantanaFabrício Santana
183
asked Mar 20 at 0:03
Fabrício SantanaFabrício Santana
183
asked Mar 20 at 0:03
Fabrício SantanaFabrício Santana
183
183
1
$begingroup$
Do you know what conditional probability is?
$endgroup$
– David M.
Mar 20 at 0:11
add a comment |
1
$begingroup$
Do you know what conditional probability is?
$endgroup$
– David M.
Mar 20 at 0:11
1
1
$begingroup$
Do you know what conditional probability is?
$endgroup$
– David M.
Mar 20 at 0:11
$begingroup$
Do you know what conditional probability is?
$endgroup$
– David M.
Mar 20 at 0:11
add a comment |
1 Answer
1
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$begingroup$
This is the so called Markov property of a system. It means that the probabilty to go to state $X_{t+1}=x_{t+1}$ does not depend on the whole history of states. If a system has the Markov property the probability will just depend on the previous state and the next state. Or to put it differently the previous state has all the informations that are necessary to describe what the system will do in the next time step.
answered Mar 20 at 0:12
MachineLearnerMachineLearner
1,319112
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add a comment |
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1 Answer
1
active
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active
oldest
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$begingroup$
This is the so called Markov property of a system. It means that the probabilty to go to state $X_{t+1}=x_{t+1}$ does not depend on the whole history of states. If a system has the Markov property the probability will just depend on the previous state and the next state. Or to put it differently the previous state has all the informations that are necessary to describe what the system will do in the next time step.
answered Mar 20 at 0:12
MachineLearnerMachineLearner
1,319112
$endgroup$
add a comment |
$begingroup$
This is the so called Markov property of a system. It means that the probabilty to go to state $X_{t+1}=x_{t+1}$ does not depend on the whole history of states. If a system has the Markov property the probability will just depend on the previous state and the next state. Or to put it differently the previous state has all the informations that are necessary to describe what the system will do in the next time step.
answered Mar 20 at 0:12
MachineLearnerMachineLearner
1,319112
$endgroup$
add a comment |
$begingroup$
This is the so called Markov property of a system. It means that the probabilty to go to state $X_{t+1}=x_{t+1}$ does not depend on the whole history of states. If a system has the Markov property the probability will just depend on the previous state and the next state. Or to put it differently the previous state has all the informations that are necessary to describe what the system will do in the next time step.
answered Mar 20 at 0:12
MachineLearnerMachineLearner
1,319112
$endgroup$
This is the so called Markov property of a system. It means that the probabilty to go to state $X_{t+1}=x_{t+1}$ does not depend on the whole history of states. If a system has the Markov property the probability will just depend on the previous state and the next state. Or to put it differently the previous state has all the informations that are necessary to describe what the system will do in the next time step.
answered Mar 20 at 0:12
MachineLearnerMachineLearner
1,319112
answered Mar 20 at 0:12
MachineLearnerMachineLearner
1,319112
answered Mar 20 at 0:12
MachineLearnerMachineLearner
1,319112
answered Mar 20 at 0:12
MachineLearnerMachineLearner
1,319112
1,319112
add a comment |
add a comment |
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Do you know what conditional probability is?
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– David M.
Mar 20 at 0:11