Choosing between non-determinism and probabilistic models
I have a stochastic system such that there are discrete states. At each discrete state, one or more probabilistic transition rules apply. For example, when Sam is in house he can go to school with a probability of 0.8 and college 0.2, or he can eat fish with probability 0.6 and Excercise 0.4.
So in such a situation, we can resolve the conflict in two ways: probabilistically or nondeterministically.
Suppose at a state 3 probabilistic transition rules are permissible. Then the probabilistic way to resolve the conflict is to say each transition rule is executed with 1/3 probability. So in the previous case, Sam can eat fish with the probability of $0.5times 0.6=0.3$. A second way is to just let it be nondeterministic. In such cases, all we can ask about the system is its maximum or minimum because there is no one expected value. In technical words, these are multiple adversaries of a Markov decision process and cannot be condensed to a single state.
My question is: which approach is better industry-wise or in real-world applications (not interested in academic applications) and why? I have read on forums that second is better.
stochastic-processes markov-chains markov-process
add a comment |
I have a stochastic system such that there are discrete states. At each discrete state, one or more probabilistic transition rules apply. For example, when Sam is in house he can go to school with a probability of 0.8 and college 0.2, or he can eat fish with probability 0.6 and Excercise 0.4.
So in such a situation, we can resolve the conflict in two ways: probabilistically or nondeterministically.
Suppose at a state 3 probabilistic transition rules are permissible. Then the probabilistic way to resolve the conflict is to say each transition rule is executed with 1/3 probability. So in the previous case, Sam can eat fish with the probability of $0.5times 0.6=0.3$. A second way is to just let it be nondeterministic. In such cases, all we can ask about the system is its maximum or minimum because there is no one expected value. In technical words, these are multiple adversaries of a Markov decision process and cannot be condensed to a single state.
My question is: which approach is better industry-wise or in real-world applications (not interested in academic applications) and why? I have read on forums that second is better.
stochastic-processes markov-chains markov-process
If you can assign probabilities, use a probabilistic model. Otherwise, it is simply indeterminate.
– herb steinberg
Nov 27 '18 at 22:25
add a comment |
I have a stochastic system such that there are discrete states. At each discrete state, one or more probabilistic transition rules apply. For example, when Sam is in house he can go to school with a probability of 0.8 and college 0.2, or he can eat fish with probability 0.6 and Excercise 0.4.
So in such a situation, we can resolve the conflict in two ways: probabilistically or nondeterministically.
Suppose at a state 3 probabilistic transition rules are permissible. Then the probabilistic way to resolve the conflict is to say each transition rule is executed with 1/3 probability. So in the previous case, Sam can eat fish with the probability of $0.5times 0.6=0.3$. A second way is to just let it be nondeterministic. In such cases, all we can ask about the system is its maximum or minimum because there is no one expected value. In technical words, these are multiple adversaries of a Markov decision process and cannot be condensed to a single state.
My question is: which approach is better industry-wise or in real-world applications (not interested in academic applications) and why? I have read on forums that second is better.
stochastic-processes markov-chains markov-process
I have a stochastic system such that there are discrete states. At each discrete state, one or more probabilistic transition rules apply. For example, when Sam is in house he can go to school with a probability of 0.8 and college 0.2, or he can eat fish with probability 0.6 and Excercise 0.4.
So in such a situation, we can resolve the conflict in two ways: probabilistically or nondeterministically.
Suppose at a state 3 probabilistic transition rules are permissible. Then the probabilistic way to resolve the conflict is to say each transition rule is executed with 1/3 probability. So in the previous case, Sam can eat fish with the probability of $0.5times 0.6=0.3$. A second way is to just let it be nondeterministic. In such cases, all we can ask about the system is its maximum or minimum because there is no one expected value. In technical words, these are multiple adversaries of a Markov decision process and cannot be condensed to a single state.
My question is: which approach is better industry-wise or in real-world applications (not interested in academic applications) and why? I have read on forums that second is better.
stochastic-processes markov-chains markov-process
stochastic-processes markov-chains markov-process
asked Nov 27 '18 at 21:46
user_1_1_1user_1_1_1
313311
313311
If you can assign probabilities, use a probabilistic model. Otherwise, it is simply indeterminate.
– herb steinberg
Nov 27 '18 at 22:25
add a comment |
If you can assign probabilities, use a probabilistic model. Otherwise, it is simply indeterminate.
– herb steinberg
Nov 27 '18 at 22:25
If you can assign probabilities, use a probabilistic model. Otherwise, it is simply indeterminate.
– herb steinberg
Nov 27 '18 at 22:25
If you can assign probabilities, use a probabilistic model. Otherwise, it is simply indeterminate.
– herb steinberg
Nov 27 '18 at 22:25
add a comment |
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If you can assign probabilities, use a probabilistic model. Otherwise, it is simply indeterminate.
– herb steinberg
Nov 27 '18 at 22:25