Undestanding Bayesian network with OpenMarkov
$begingroup$
I downloaded OpenMarkov software for probabilistic graphical models and tried it on mtcars dataset.
The mtcars.csv data looks like this:
In OpenMarkov GUI, I went to Tools > Learning and loaded mtcars.csv dataset. I then adjusted preprocessing settings to have Discretize with Equal width intervals for all variables.
I then chose Hill Climbing algorithm (default) and Automatic learning options. On learning, the result was as follows:
My question is what exactly does this figure represent? Does it represent a Bayesian network or some other type of probabilistic graphical models? Also, do arrows mean that hp affects cyl and carb; and cyl in turn affects disp and carb and so on?
bayesian-networks markov
asked Nov 27 '18 at 15:35
rnsornso
466114
$endgroup$
add a comment |
$begingroup$
I downloaded OpenMarkov software for probabilistic graphical models and tried it on mtcars dataset.
The mtcars.csv data looks like this:
In OpenMarkov GUI, I went to Tools > Learning and loaded mtcars.csv dataset. I then adjusted preprocessing settings to have Discretize with Equal width intervals for all variables.
I then chose Hill Climbing algorithm (default) and Automatic learning options. On learning, the result was as follows:
My question is what exactly does this figure represent? Does it represent a Bayesian network or some other type of probabilistic graphical models? Also, do arrows mean that hp affects cyl and carb; and cyl in turn affects disp and carb and so on?
bayesian-networks markov
asked Nov 27 '18 at 15:35
rnsornso
466114
$endgroup$
add a comment |
$begingroup$
I downloaded OpenMarkov software for probabilistic graphical models and tried it on mtcars dataset.
The mtcars.csv data looks like this:
In OpenMarkov GUI, I went to Tools > Learning and loaded mtcars.csv dataset. I then adjusted preprocessing settings to have Discretize with Equal width intervals for all variables.
I then chose Hill Climbing algorithm (default) and Automatic learning options. On learning, the result was as follows:
My question is what exactly does this figure represent? Does it represent a Bayesian network or some other type of probabilistic graphical models? Also, do arrows mean that hp affects cyl and carb; and cyl in turn affects disp and carb and so on?
bayesian-networks markov
asked Nov 27 '18 at 15:35
rnsornso
466114
$endgroup$
I downloaded OpenMarkov software for probabilistic graphical models and tried it on mtcars dataset.
The mtcars.csv data looks like this:
In OpenMarkov GUI, I went to Tools > Learning and loaded mtcars.csv dataset. I then adjusted preprocessing settings to have Discretize with Equal width intervals for all variables.
I then chose Hill Climbing algorithm (default) and Automatic learning options. On learning, the result was as follows:
My question is what exactly does this figure represent? Does it represent a Bayesian network or some other type of probabilistic graphical models? Also, do arrows mean that hp affects cyl and carb; and cyl in turn affects disp and carb and so on?
bayesian-networks markov
bayesian-networks markov
asked Nov 27 '18 at 15:35
rnsornso
466114
asked Nov 27 '18 at 15:35
rnsornso
466114
asked Nov 27 '18 at 15:35
rnsornso
466114
asked Nov 27 '18 at 15:35
rnsornso
466114
asked Nov 27 '18 at 15:35
rnsornso
466114
466114
add a comment |
add a comment |
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$begingroup$
First off, I did not know OpenMarkov. Anyway, from its website it has a particular focus on learning Bayesian networks (Bayes nets). Thus, I assume your figure represents a Bayes net, yes. Syntactically, it also qualifies since it is a directed acyclic graph.
The arrows (edges) represent influences (conditional dependencies) observed in the data.
For instance, the conditional probability distribution of carb, P(carb | hp,cyl,disp), is defined by the values for hp,cyl,disp. However, arrows do not necessarily represent causal relationships.
answered yesterday
John QJohn Q
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John Q is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
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1 Answer
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1 Answer
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$begingroup$
First off, I did not know OpenMarkov. Anyway, from its website it has a particular focus on learning Bayesian networks (Bayes nets). Thus, I assume your figure represents a Bayes net, yes. Syntactically, it also qualifies since it is a directed acyclic graph.
The arrows (edges) represent influences (conditional dependencies) observed in the data.
For instance, the conditional probability distribution of carb, P(carb | hp,cyl,disp), is defined by the values for hp,cyl,disp. However, arrows do not necessarily represent causal relationships.
answered yesterday
John QJohn Q
214
New contributor
John Q is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
$begingroup$
First off, I did not know OpenMarkov. Anyway, from its website it has a particular focus on learning Bayesian networks (Bayes nets). Thus, I assume your figure represents a Bayes net, yes. Syntactically, it also qualifies since it is a directed acyclic graph.
The arrows (edges) represent influences (conditional dependencies) observed in the data.
For instance, the conditional probability distribution of carb, P(carb | hp,cyl,disp), is defined by the values for hp,cyl,disp. However, arrows do not necessarily represent causal relationships.
answered yesterday
John QJohn Q
214
New contributor
John Q is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
$begingroup$
First off, I did not know OpenMarkov. Anyway, from its website it has a particular focus on learning Bayesian networks (Bayes nets). Thus, I assume your figure represents a Bayes net, yes. Syntactically, it also qualifies since it is a directed acyclic graph.
The arrows (edges) represent influences (conditional dependencies) observed in the data.
For instance, the conditional probability distribution of carb, P(carb | hp,cyl,disp), is defined by the values for hp,cyl,disp. However, arrows do not necessarily represent causal relationships.
answered yesterday
John QJohn Q
214
New contributor
John Q is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
First off, I did not know OpenMarkov. Anyway, from its website it has a particular focus on learning Bayesian networks (Bayes nets). Thus, I assume your figure represents a Bayes net, yes. Syntactically, it also qualifies since it is a directed acyclic graph.
The arrows (edges) represent influences (conditional dependencies) observed in the data.
For instance, the conditional probability distribution of carb, P(carb | hp,cyl,disp), is defined by the values for hp,cyl,disp. However, arrows do not necessarily represent causal relationships.
answered yesterday
John QJohn Q
214
New contributor
John Q is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
answered yesterday
John QJohn Q
214
New contributor
John Q is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
answered yesterday
John QJohn Q
214
answered yesterday
John QJohn Q
214
214
New contributor
John Q is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
John Q is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
John Q is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
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