Influence diagrams / Decision models in Stan and PyMC3












0















Is it possible to write decision-making models in either Stan or PyMC3? By that I mean: we define not only the distribution of random variables, but also the definition of decision and utility variables, and determine the decisions maximizing expected utility.



My understanding is that Stan is more of a general optimizer than PyMC3, so that suggests decision models would be more directly implemented in it, but I would like to hear what people have to say.



Edit: While it is possible to enumerate all decisions and compute their corresponding expected utility, I am wondering about more efficient methods since the number of decisions could be combinatorially too many (for example, how many items to buy from a list with thousands of products). Influence diagram algorithms exploit factorizations in the model to identify independences that allow computing of the decisions on only a smaller set of relevant random variables. I wonder if either Stan or PyMC3 do that kind of thing.






pymc3 stan markov-decision-process probabilistic-programming







share|improve this question

























  • Stan has optimization algorithms, but they are quite basic (aside from using autodifferentiation to calculate the gradient), and the MCMC sampling algorithms are more suitable for decision theory anyway.

    – Ben Goodrich
    Nov 18 '18 at 5:58
















0















Is it possible to write decision-making models in either Stan or PyMC3? By that I mean: we define not only the distribution of random variables, but also the definition of decision and utility variables, and determine the decisions maximizing expected utility.



My understanding is that Stan is more of a general optimizer than PyMC3, so that suggests decision models would be more directly implemented in it, but I would like to hear what people have to say.



Edit: While it is possible to enumerate all decisions and compute their corresponding expected utility, I am wondering about more efficient methods since the number of decisions could be combinatorially too many (for example, how many items to buy from a list with thousands of products). Influence diagram algorithms exploit factorizations in the model to identify independences that allow computing of the decisions on only a smaller set of relevant random variables. I wonder if either Stan or PyMC3 do that kind of thing.






pymc3 stan markov-decision-process probabilistic-programming







share|improve this question

























  • Stan has optimization algorithms, but they are quite basic (aside from using autodifferentiation to calculate the gradient), and the MCMC sampling algorithms are more suitable for decision theory anyway.

    – Ben Goodrich
    Nov 18 '18 at 5:58














0












0








0








Is it possible to write decision-making models in either Stan or PyMC3? By that I mean: we define not only the distribution of random variables, but also the definition of decision and utility variables, and determine the decisions maximizing expected utility.



My understanding is that Stan is more of a general optimizer than PyMC3, so that suggests decision models would be more directly implemented in it, but I would like to hear what people have to say.



Edit: While it is possible to enumerate all decisions and compute their corresponding expected utility, I am wondering about more efficient methods since the number of decisions could be combinatorially too many (for example, how many items to buy from a list with thousands of products). Influence diagram algorithms exploit factorizations in the model to identify independences that allow computing of the decisions on only a smaller set of relevant random variables. I wonder if either Stan or PyMC3 do that kind of thing.






pymc3 stan markov-decision-process probabilistic-programming







share|improve this question
















Is it possible to write decision-making models in either Stan or PyMC3? By that I mean: we define not only the distribution of random variables, but also the definition of decision and utility variables, and determine the decisions maximizing expected utility.



My understanding is that Stan is more of a general optimizer than PyMC3, so that suggests decision models would be more directly implemented in it, but I would like to hear what people have to say.



Edit: While it is possible to enumerate all decisions and compute their corresponding expected utility, I am wondering about more efficient methods since the number of decisions could be combinatorially too many (for example, how many items to buy from a list with thousands of products). Influence diagram algorithms exploit factorizations in the model to identify independences that allow computing of the decisions on only a smaller set of relevant random variables. I wonder if either Stan or PyMC3 do that kind of thing.






pymc3 stan markov-decision-process probabilistic-programming




pymc3 stan markov-decision-process probabilistic-programming






share|improve this question















share|improve this question













share|improve this question




share|improve this question








edited Nov 18 '18 at 19:07







user118967

















asked Nov 17 '18 at 23:55









user118967user118967

558416




558416













  • Stan has optimization algorithms, but they are quite basic (aside from using autodifferentiation to calculate the gradient), and the MCMC sampling algorithms are more suitable for decision theory anyway.

    – Ben Goodrich
    Nov 18 '18 at 5:58



















  • Stan has optimization algorithms, but they are quite basic (aside from using autodifferentiation to calculate the gradient), and the MCMC sampling algorithms are more suitable for decision theory anyway.

    – Ben Goodrich
    Nov 18 '18 at 5:58

















Stan has optimization algorithms, but they are quite basic (aside from using autodifferentiation to calculate the gradient), and the MCMC sampling algorithms are more suitable for decision theory anyway.

– Ben Goodrich
Nov 18 '18 at 5:58





Stan has optimization algorithms, but they are quite basic (aside from using autodifferentiation to calculate the gradient), and the MCMC sampling algorithms are more suitable for decision theory anyway.

– Ben Goodrich
Nov 18 '18 at 5:58












1 Answer
1






active

oldest

votes


















2














The basic steps for Bayesian decision theory are:




  1. Enumerate a finite set of decisions that could be made

  2. Specify a utility function of the decision and perhaps other things

  3. Draw from the posterior distribution of all the unknowns given the known data

  4. Evaluate the utility function for each possible decision and each posterior draw

  5. Make the decision with the highest expected utility, averaging over the posterior draws.


You can do those five steps with any software --- Stan and PyMC3 included --- that produces (valid) draws from the posterior distribution. In Stan, the utility function should be evaluated in the generated quantities block.






share|improve this answer
























  • You are right. However, I was thinking of more efficient methods than simply enumerating the decisions, which could be combinatorially too many (for example, how many items to buy from a list with thousands of products). Influence diagram algorithms exploit factorizations in the model to identify independences that allow some of the decisions to be computed from a smaller set of relevant random variables, and that's what I had in mind. I will update the question to that effect.

    – user118967
    Nov 18 '18 at 19:04








  • 1





    Stan does not involve graphs at all, so if there are relevant conditional independencies, then users have to recognize them and utilize them. PyMC3 does have some graph capabilities, but I don't know if they are sufficient for your use case.

    – Ben Goodrich
    Nov 19 '18 at 20:05











Your Answer






StackExchange.ifUsing("editor", function () {
StackExchange.using("externalEditor", function () {
StackExchange.using("snippets", function () {
StackExchange.snippets.init();
});
});
}, "code-snippets");

StackExchange.ready(function() {
var channelOptions = {
tags: "".split(" "),
id: "1"
};
initTagRenderer("".split(" "), "".split(" "), channelOptions);

StackExchange.using("externalEditor", function() {
// Have to fire editor after snippets, if snippets enabled
if (StackExchange.settings.snippets.snippetsEnabled) {
StackExchange.using("snippets", function() {
createEditor();
});
}
else {
createEditor();
}
});

function createEditor() {
StackExchange.prepareEditor({
heartbeatType: 'answer',
autoActivateHeartbeat: false,
convertImagesToLinks: true,
noModals: true,
showLowRepImageUploadWarning: true,
reputationToPostImages: 10,
bindNavPrevention: true,
postfix: "",
imageUploader: {
brandingHtml: "Powered by u003ca class="icon-imgur-white" href="https://imgur.com/"u003eu003c/au003e",
contentPolicyHtml: "User contributions licensed under u003ca href="https://creativecommons.org/licenses/by-sa/3.0/"u003ecc by-sa 3.0 with attribution requiredu003c/au003e u003ca href="https://stackoverflow.com/legal/content-policy"u003e(content policy)u003c/au003e",
allowUrls: true
},
onDemand: true,
discardSelector: ".discard-answer"
,immediatelyShowMarkdownHelp:true
});


}
});














draft saved

draft discarded


















StackExchange.ready(
function () {
StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fstackoverflow.com%2fquestions%2f53356699%2finfluence-diagrams-decision-models-in-stan-and-pymc3%23new-answer', 'question_page');
}
);

Post as a guest















Required, but never shown

























1 Answer
1






active

oldest

votes








1 Answer
1






active

oldest

votes









active

oldest

votes






active

oldest

votes









2














The basic steps for Bayesian decision theory are:




  1. Enumerate a finite set of decisions that could be made

  2. Specify a utility function of the decision and perhaps other things

  3. Draw from the posterior distribution of all the unknowns given the known data

  4. Evaluate the utility function for each possible decision and each posterior draw

  5. Make the decision with the highest expected utility, averaging over the posterior draws.


You can do those five steps with any software --- Stan and PyMC3 included --- that produces (valid) draws from the posterior distribution. In Stan, the utility function should be evaluated in the generated quantities block.






share|improve this answer
























  • You are right. However, I was thinking of more efficient methods than simply enumerating the decisions, which could be combinatorially too many (for example, how many items to buy from a list with thousands of products). Influence diagram algorithms exploit factorizations in the model to identify independences that allow some of the decisions to be computed from a smaller set of relevant random variables, and that's what I had in mind. I will update the question to that effect.

    – user118967
    Nov 18 '18 at 19:04








  • 1





    Stan does not involve graphs at all, so if there are relevant conditional independencies, then users have to recognize them and utilize them. PyMC3 does have some graph capabilities, but I don't know if they are sufficient for your use case.

    – Ben Goodrich
    Nov 19 '18 at 20:05
















2














The basic steps for Bayesian decision theory are:




  1. Enumerate a finite set of decisions that could be made

  2. Specify a utility function of the decision and perhaps other things

  3. Draw from the posterior distribution of all the unknowns given the known data

  4. Evaluate the utility function for each possible decision and each posterior draw

  5. Make the decision with the highest expected utility, averaging over the posterior draws.


You can do those five steps with any software --- Stan and PyMC3 included --- that produces (valid) draws from the posterior distribution. In Stan, the utility function should be evaluated in the generated quantities block.






share|improve this answer
























  • You are right. However, I was thinking of more efficient methods than simply enumerating the decisions, which could be combinatorially too many (for example, how many items to buy from a list with thousands of products). Influence diagram algorithms exploit factorizations in the model to identify independences that allow some of the decisions to be computed from a smaller set of relevant random variables, and that's what I had in mind. I will update the question to that effect.

    – user118967
    Nov 18 '18 at 19:04








  • 1





    Stan does not involve graphs at all, so if there are relevant conditional independencies, then users have to recognize them and utilize them. PyMC3 does have some graph capabilities, but I don't know if they are sufficient for your use case.

    – Ben Goodrich
    Nov 19 '18 at 20:05














2












2








2







The basic steps for Bayesian decision theory are:




  1. Enumerate a finite set of decisions that could be made

  2. Specify a utility function of the decision and perhaps other things

  3. Draw from the posterior distribution of all the unknowns given the known data

  4. Evaluate the utility function for each possible decision and each posterior draw

  5. Make the decision with the highest expected utility, averaging over the posterior draws.


You can do those five steps with any software --- Stan and PyMC3 included --- that produces (valid) draws from the posterior distribution. In Stan, the utility function should be evaluated in the generated quantities block.






share|improve this answer













The basic steps for Bayesian decision theory are:




  1. Enumerate a finite set of decisions that could be made

  2. Specify a utility function of the decision and perhaps other things

  3. Draw from the posterior distribution of all the unknowns given the known data

  4. Evaluate the utility function for each possible decision and each posterior draw

  5. Make the decision with the highest expected utility, averaging over the posterior draws.


You can do those five steps with any software --- Stan and PyMC3 included --- that produces (valid) draws from the posterior distribution. In Stan, the utility function should be evaluated in the generated quantities block.







share|improve this answer












share|improve this answer



share|improve this answer










answered Nov 18 '18 at 5:58









Ben GoodrichBen Goodrich

3,1221813




3,1221813













  • You are right. However, I was thinking of more efficient methods than simply enumerating the decisions, which could be combinatorially too many (for example, how many items to buy from a list with thousands of products). Influence diagram algorithms exploit factorizations in the model to identify independences that allow some of the decisions to be computed from a smaller set of relevant random variables, and that's what I had in mind. I will update the question to that effect.

    – user118967
    Nov 18 '18 at 19:04








  • 1





    Stan does not involve graphs at all, so if there are relevant conditional independencies, then users have to recognize them and utilize them. PyMC3 does have some graph capabilities, but I don't know if they are sufficient for your use case.

    – Ben Goodrich
    Nov 19 '18 at 20:05



















  • You are right. However, I was thinking of more efficient methods than simply enumerating the decisions, which could be combinatorially too many (for example, how many items to buy from a list with thousands of products). Influence diagram algorithms exploit factorizations in the model to identify independences that allow some of the decisions to be computed from a smaller set of relevant random variables, and that's what I had in mind. I will update the question to that effect.

    – user118967
    Nov 18 '18 at 19:04








  • 1





    Stan does not involve graphs at all, so if there are relevant conditional independencies, then users have to recognize them and utilize them. PyMC3 does have some graph capabilities, but I don't know if they are sufficient for your use case.

    – Ben Goodrich
    Nov 19 '18 at 20:05

















You are right. However, I was thinking of more efficient methods than simply enumerating the decisions, which could be combinatorially too many (for example, how many items to buy from a list with thousands of products). Influence diagram algorithms exploit factorizations in the model to identify independences that allow some of the decisions to be computed from a smaller set of relevant random variables, and that's what I had in mind. I will update the question to that effect.

– user118967
Nov 18 '18 at 19:04







You are right. However, I was thinking of more efficient methods than simply enumerating the decisions, which could be combinatorially too many (for example, how many items to buy from a list with thousands of products). Influence diagram algorithms exploit factorizations in the model to identify independences that allow some of the decisions to be computed from a smaller set of relevant random variables, and that's what I had in mind. I will update the question to that effect.

– user118967
Nov 18 '18 at 19:04






1




1





Stan does not involve graphs at all, so if there are relevant conditional independencies, then users have to recognize them and utilize them. PyMC3 does have some graph capabilities, but I don't know if they are sufficient for your use case.

– Ben Goodrich
Nov 19 '18 at 20:05





Stan does not involve graphs at all, so if there are relevant conditional independencies, then users have to recognize them and utilize them. PyMC3 does have some graph capabilities, but I don't know if they are sufficient for your use case.

– Ben Goodrich
Nov 19 '18 at 20:05


















draft saved

draft discarded




















































Thanks for contributing an answer to Stack Overflow!


  • Please be sure to answer the question. Provide details and share your research!

But avoid …



  • Asking for help, clarification, or responding to other answers.

  • Making statements based on opinion; back them up with references or personal experience.


To learn more, see our tips on writing great answers.




draft saved


draft discarded














StackExchange.ready(
function () {
StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fstackoverflow.com%2fquestions%2f53356699%2finfluence-diagrams-decision-models-in-stan-and-pymc3%23new-answer', 'question_page');
}
);

Post as a guest















Required, but never shown





















































Required, but never shown














Required, but never shown












Required, but never shown







Required, but never shown

































Required, but never shown














Required, but never shown












Required, but never shown







Required, but never shown







-

Popular posts from this blog

Guess what letter conforming each word

Image
6 $begingroup$ This is an entry in the Fortnightly Topic Challenge #41: Short and Sweet. 1 letter only could be mean anything . With an additional letter in front , could be used as medicine component and something you definitely learn or at least heard once. Add another letter in front , you would possibly hear that many times . Add an additional letter in front , you would found an independent country . With an additional letter, in the end , you would found something related to apple . Add another letter in the end , you would found something bad . Hint: Each letter is unique . word knowledge letters chemistry share | improve this question ...
Read more

Port of Spain

Image
For ports in the country of Spain, see List of ports in Spain. City in City of Port of Spain, Trinidad and Tobago Port of Spain City City of Port of Spain Top to bottom (left to right): Skyline of Port of Spain from Gulf of Paria; National Academy for the Performing Arts, Independence Square, Eric Williams Plaza; MovieTowne, Port of Port of Spain Motto(s):  "We Gather Strength As We Go Along" Port of Spain Coordinates: 10°40′N 61°31′W  /  10.667°N 61.517°W  / 10.667; -61.517 Coordinates: 10°40′N 61°31′W  /  10.667°N 61.517°W  / 10.667; -61.517 Country   Trinidad and Tobago Jurisdiction City of Port of Spain Settled 1560 Incorporated (town) 1853 Incorporated (city) 1914 Government  • Mayor Joel Martinez  • Governing body City Corporation Area [1]  • City 12 km 2 (5 sq mi) Elevation [2] 66 m (217 ft) Population (2011) [1]  • City 37,074  • Rank 3rd  • Density 3,090/km 2 (8,000/sq mi)...
Read more

Run scheduled task as local user group (not BUILTIN)

Image
0 I am trying to run a scheduled task with User Group (not a single user). This is working fine when I in the field "when running a task, use the following user account:" insert BUILTIN User Group (for example: Administrators or Users). But when I try it with the created Local User Group (example: TestGroup) it is not working. The Users in the TestGroup are the same as in the Administrators group. What could be the problem? Are there some restrictions to the Created User Group compared to BUILTINUsers? scheduled-tasks taskscheduler windowstaskschedule share | improve this question asked Nov 15 '18 at 15:36 Far...
Read more