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Does Approximate Bayesian Computation (ABC) follow the Likelihood Principle?


What is an “uninformative prior”? Can we ever have one with truly no information?ABC. How can it avoid the likelihood function?Parameter Estimation for intractable Likelihoods / Alternatives to approximate Bayesian computationDistance metric for Approximate Bayesian Computation (ABC) regressionApproximate Bayesian computation: where to start from?Simple linear regression using Approximate Bayesian Computation (ABC)In what situations would one use Approximate Bayesian Computation instead of Bayesian inference?Using maximum Likelihood regression to get closer to the true posterior when doing Approximate Bayesian Computation : contradiction?Approximate Bayesian Computation for parameters estimation in ODE-based modelApproximate bayesian computation: model selection on nested modelsProof of Approximate / Exact Bayesian Computation













6












$begingroup$


I know that ABC is commonly used when the likelihood is intractable, so likelihood principle is not an interest in that case. But, I am curious whether the ABC satisfies the likelihood principle when the likelihood function is tractable. ABC is a generative procedure to sample parameters from posterior, and likelihood principle says that the inference on the parameter should be solely determined by likelihood part ignoring the term of the observation.



I think that if I generate fake samples from a parameter, the generating process is crucially affected by the term of observation, which might be ignored in the likelihood principle.



It's confusing, because I think that the ABC does not follow the likelihood principle, but it is well-known that Bayesian stat follows it.



Am I missing something?






bayesian computational-statistics abc







share|cite|improve this question











$endgroup$
















    6












    $begingroup$


    I know that ABC is commonly used when the likelihood is intractable, so likelihood principle is not an interest in that case. But, I am curious whether the ABC satisfies the likelihood principle when the likelihood function is tractable. ABC is a generative procedure to sample parameters from posterior, and likelihood principle says that the inference on the parameter should be solely determined by likelihood part ignoring the term of the observation.



    I think that if I generate fake samples from a parameter, the generating process is crucially affected by the term of observation, which might be ignored in the likelihood principle.



    It's confusing, because I think that the ABC does not follow the likelihood principle, but it is well-known that Bayesian stat follows it.



    Am I missing something?






    bayesian computational-statistics abc







    share|cite|improve this question











    $endgroup$














      6












      6








      6


      1



      $begingroup$


      I know that ABC is commonly used when the likelihood is intractable, so likelihood principle is not an interest in that case. But, I am curious whether the ABC satisfies the likelihood principle when the likelihood function is tractable. ABC is a generative procedure to sample parameters from posterior, and likelihood principle says that the inference on the parameter should be solely determined by likelihood part ignoring the term of the observation.



      I think that if I generate fake samples from a parameter, the generating process is crucially affected by the term of observation, which might be ignored in the likelihood principle.



      It's confusing, because I think that the ABC does not follow the likelihood principle, but it is well-known that Bayesian stat follows it.



      Am I missing something?






      bayesian computational-statistics abc







      share|cite|improve this question











      $endgroup$




      I know that ABC is commonly used when the likelihood is intractable, so likelihood principle is not an interest in that case. But, I am curious whether the ABC satisfies the likelihood principle when the likelihood function is tractable. ABC is a generative procedure to sample parameters from posterior, and likelihood principle says that the inference on the parameter should be solely determined by likelihood part ignoring the term of the observation.



      I think that if I generate fake samples from a parameter, the generating process is crucially affected by the term of observation, which might be ignored in the likelihood principle.



      It's confusing, because I think that the ABC does not follow the likelihood principle, but it is well-known that Bayesian stat follows it.



      Am I missing something?






      bayesian computational-statistics abc




      bayesian computational-statistics abc






      share|cite|improve this question















      share|cite|improve this question













      share|cite|improve this question




      share|cite|improve this question








      edited 19 hours ago







      Minsuk Shin

















      asked 19 hours ago









      Minsuk ShinMinsuk Shin

      663




      663




















          1 Answer
          1






          active

          oldest

          votes


















          2












          $begingroup$

          The "when the likelihood function is tractable" is somewhat self-defeating, as the reason for using ABC is that it is intractable.



          As for the likelihood principle, ABC is definitely not respecting it, since it requires a simulation of the data from its sampling distribution. It thus uses the frequentist properties of that distribution rather than the likelihood itself. Except in the (unrealistic) limiting case when the tolerance is exactly zero and the distance is based on the sufficient statistic, the ABC thus fails to agree with the likelihood principle.



          In my humble opinion, this is a minor issue when compared with the major problems faced by ABC, unless you can provide an example with dire (There are also exact Bayesian approaches that do not agree with the likelihood principle, witness the Jeffreys or matching priors.)






          share|cite|improve this answer











          $endgroup$













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            $begingroup$

            The "when the likelihood function is tractable" is somewhat self-defeating, as the reason for using ABC is that it is intractable.



            As for the likelihood principle, ABC is definitely not respecting it, since it requires a simulation of the data from its sampling distribution. It thus uses the frequentist properties of that distribution rather than the likelihood itself. Except in the (unrealistic) limiting case when the tolerance is exactly zero and the distance is based on the sufficient statistic, the ABC thus fails to agree with the likelihood principle.



            In my humble opinion, this is a minor issue when compared with the major problems faced by ABC, unless you can provide an example with dire (There are also exact Bayesian approaches that do not agree with the likelihood principle, witness the Jeffreys or matching priors.)






            share|cite|improve this answer











            $endgroup$

















              2












              $begingroup$

              The "when the likelihood function is tractable" is somewhat self-defeating, as the reason for using ABC is that it is intractable.



              As for the likelihood principle, ABC is definitely not respecting it, since it requires a simulation of the data from its sampling distribution. It thus uses the frequentist properties of that distribution rather than the likelihood itself. Except in the (unrealistic) limiting case when the tolerance is exactly zero and the distance is based on the sufficient statistic, the ABC thus fails to agree with the likelihood principle.



              In my humble opinion, this is a minor issue when compared with the major problems faced by ABC, unless you can provide an example with dire (There are also exact Bayesian approaches that do not agree with the likelihood principle, witness the Jeffreys or matching priors.)






              share|cite|improve this answer











              $endgroup$















                2












                2








                2





                $begingroup$

                The "when the likelihood function is tractable" is somewhat self-defeating, as the reason for using ABC is that it is intractable.



                As for the likelihood principle, ABC is definitely not respecting it, since it requires a simulation of the data from its sampling distribution. It thus uses the frequentist properties of that distribution rather than the likelihood itself. Except in the (unrealistic) limiting case when the tolerance is exactly zero and the distance is based on the sufficient statistic, the ABC thus fails to agree with the likelihood principle.



                In my humble opinion, this is a minor issue when compared with the major problems faced by ABC, unless you can provide an example with dire (There are also exact Bayesian approaches that do not agree with the likelihood principle, witness the Jeffreys or matching priors.)






                share|cite|improve this answer











                $endgroup$



                The "when the likelihood function is tractable" is somewhat self-defeating, as the reason for using ABC is that it is intractable.



                As for the likelihood principle, ABC is definitely not respecting it, since it requires a simulation of the data from its sampling distribution. It thus uses the frequentist properties of that distribution rather than the likelihood itself. Except in the (unrealistic) limiting case when the tolerance is exactly zero and the distance is based on the sufficient statistic, the ABC thus fails to agree with the likelihood principle.



                In my humble opinion, this is a minor issue when compared with the major problems faced by ABC, unless you can provide an example with dire (There are also exact Bayesian approaches that do not agree with the likelihood principle, witness the Jeffreys or matching priors.)







                share|cite|improve this answer














                share|cite|improve this answer



                share|cite|improve this answer








                edited 13 hours ago

























                answered 18 hours ago









                Xi'anXi'an

                59k897365




                59k897365



























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