Inferring state of a system from multiple agent input
I need help with identifying methodology, approach, and data requirements for a problem that I am trying to solve. I have a dynamic process in which the state of the system can take either 0 or 1. I have ‘n’ agents observing the system and reporting back to me the state. Each agent may have different credibility which I do not know to begin with. My aim is to infer the probability of the system in a state provided the observations. What methodology, data, and approaches should I use to solve this problem?
Schematics of problem given here (image)
algorithm machine-learning bayesian-networks probabilistic-programming
asked Jan 18 at 23:48
AlexAlex
11
add a comment |
I need help with identifying methodology, approach, and data requirements for a problem that I am trying to solve. I have a dynamic process in which the state of the system can take either 0 or 1. I have ‘n’ agents observing the system and reporting back to me the state. Each agent may have different credibility which I do not know to begin with. My aim is to infer the probability of the system in a state provided the observations. What methodology, data, and approaches should I use to solve this problem?
Schematics of problem given here (image)
algorithm machine-learning bayesian-networks probabilistic-programming
asked Jan 18 at 23:48
AlexAlex
11
Looks like a Bayesian network, typically this is solved with a sum product algorithm.
– Willem Van Onsem
Jan 18 at 23:49
add a comment |
I need help with identifying methodology, approach, and data requirements for a problem that I am trying to solve. I have a dynamic process in which the state of the system can take either 0 or 1. I have ‘n’ agents observing the system and reporting back to me the state. Each agent may have different credibility which I do not know to begin with. My aim is to infer the probability of the system in a state provided the observations. What methodology, data, and approaches should I use to solve this problem?
Schematics of problem given here (image)
algorithm machine-learning bayesian-networks probabilistic-programming
asked Jan 18 at 23:48
AlexAlex
11
I need help with identifying methodology, approach, and data requirements for a problem that I am trying to solve. I have a dynamic process in which the state of the system can take either 0 or 1. I have ‘n’ agents observing the system and reporting back to me the state. Each agent may have different credibility which I do not know to begin with. My aim is to infer the probability of the system in a state provided the observations. What methodology, data, and approaches should I use to solve this problem?
Schematics of problem given here (image)
algorithm machine-learning bayesian-networks probabilistic-programming
algorithm machine-learning bayesian-networks probabilistic-programming
asked Jan 18 at 23:48
AlexAlex
11
asked Jan 18 at 23:48
AlexAlex
11
asked Jan 18 at 23:48
AlexAlex
11
asked Jan 18 at 23:48
AlexAlex
11
asked Jan 18 at 23:48
AlexAlex
11
11
Looks like a Bayesian network, typically this is solved with a sum product algorithm.
– Willem Van Onsem
Jan 18 at 23:49
add a comment |
Looks like a Bayesian network, typically this is solved with a sum product algorithm.
– Willem Van Onsem
Jan 18 at 23:49
Looks like a Bayesian network, typically this is solved with a sum product algorithm.
– Willem Van Onsem
Jan 18 at 23:49
Looks like a Bayesian network, typically this is solved with a sum product algorithm.
– Willem Van Onsem
Jan 18 at 23:49
add a comment |
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Looks like a Bayesian network, typically this is solved with a sum product algorithm.
– Willem Van Onsem
Jan 18 at 23:49