One of the most controversial approaches to statistics, this post mainly deals with the fundamental objections to Bayesian methods and Bayesian school of thinking. Turning to the Bayesian crank, Fisher put forward a vehement objection towards Bayesian Inference, describing it as "fallacious rubbish".
However, ironically enough, it’s interesting to note that Fisher’s greatest statistical failure, fiducialism, was essentially an attempt to “enjoy the Bayesian omelette without breaking any Bayesian eggs" !
An inductive logic is a logic of evidential support. In a deductive logic, the premises of a valid deductive argument logically entail the conclusion, where logical entailment means that every logically possible state of affairs that makes the premises true must make the conclusion truth as well. Thus, the premises of a valid deductive argument provide total support for the conclusion. An inductive logic extends this idea to weaker arguments. In a good inductive argument, the truth of the premises provides some degree of support for the truth of the conclusion, where this degree-of-support might be measured via some numerical scale.
If a logic of good inductive arguments is to be of any real value, the measure of support it articulates should be up to the task. Presumably, the logic should at least satisfy the following condition:
Criterion of Adequacy (CoA):
The logic should make it likely (as a matter of logic) that as evidence accumulates, the total body of true evidence claims will eventually come to indicate, via the logic’s measure of support, that false hypotheses are probably false and that true hypotheses are probably true.
One practical example of an easy inductive inference is the following:
" Every bird in a random sample of 3200 birds is black. This strongly supports the following conclusion: All birds are black. "
This kind of argument is often called an induction by enumeration. It is closely related to the technique of statistical estimation.
Non-trivial calculi of inductive inference are shown to be incomplete. That is, it is impossible for a calculus of inductive inference to capture all inductive truths in some domain, no matter how large, without resorting to inductive content drawn from outside that domain. Hence inductive inference cannot be characterized merely as inference that conforms with some specified calculus.
A probabilistic logic of induction is unable to separate cleanly neutral support from disfavoring evidence (or ignorance from disbelief). Thus, the use of probabilistic representations may introduce spurious results stemming from its expressive inadequacy. That such spurious results arise in the Bayesian "doomsday argument" is shown by a re-analysis that employs fragments of inductive logic able to represent evidential neutrality. Further, the improper introduction of inductive probabilities is illustrated with the "self-sampling assumption."
While Bayesian analysis has enjoyed notable success with many particular problems of inductive inference, it is not the one true and universal logic of induction. Some of the reasons arise at the global level through the existence of competing systems of inductive logic. Others emerge through an examination of the individual assumptions that, when combined, form the Bayesian system: that there is a real valued magnitude that expresses evidential support, that it is additive and that its treatment of logical conjunction is such that Bayes' theorem ensues.
The fundamental objections to Bayesian methods are twofold: on one hand, Bayesian methods are presented as an automatic inference engine, and this raises suspicion in anyone with applied experience. The second objection to Bayes' comes from the opposite direction and addresses the subjective strand of Bayesian inference.
Andrew Gelman , a staunch Bayesian pens down an interesting criticism of the Bayesian ideology in the voice of a hypothetical anti-Bayesian statistician.
Here is the list of objections from a hypothetical or paradigmatic non-Bayesian ; and I quote:
"Bayesian inference is a coherent mathematical theory but I don’t trust it in scientific applications. Subjective prior distributions don’t transfer well from person to person, and there’s no good objective principle for choosing a non-informative prior (even if that concept were mathematically defined, which it’s not). Where do prior distributions
come from, anyway? I don’t trust them and I see no reason to recommend that other people do, just so that I can have the warm feeling of philosophical coherence. To put it another way, why should I believe your subjective prior? If I really believed it, then I could just feed you some data and ask you for your subjective posterior. That would save me a lot of effort!"
In 1986 , a statistician as prominent as Brad Efron restates these concerns mathematically:
"I like unbiased estimates and I like confidence intervals that really have their advertised confidence coverage. I know that these aren’t always going to be possible, but I think the right way forward is to get as close to these goals as possible and to develop robust methods that work with minimal assumptions. The Bayesian approach—to give up even trying to approximate unbiasedness and to instead rely on stronger and stronger assumptions—that seems like the wrong way to go. When the priors I see in practice are typically just convenient conjugate forms. What a coincidence that, of all the infinite variety of priors that could be chosen, it always seems to be the normal, gamma, beta, etc., that turn out to be the right choices?"
Well that really sums up every frequentist's rant about Bayes' 😀 !
Some frequentists believe that in the old days, Bayesian methods at least had the virtue of being mathematically
clean. Nowadays, they all seem to be computed using Markov chain Monte Carlo, which means that, not only can you not realistically evaluate the statistical properties of the method, you can’t even be sure it’s converged, just adding one more item to the list of unverifiable (and unverified) assumptions in Bayesian belief.
As the applied statistician Andrew Ehrenberg wrote :
" Bayesianism assumes:
(a) Either a weak or uniform prior, in which case why bother?,
(b) Or a strong prior, in which case why collect new data?,
(c) Or more realistically, something in between,in which case Bayesianism always seems to duck the issue."
Many are skeptical about the new found empirical approach of Bayesians which always seems to rely on the assumption of "exchangeability", which is almost impossible to obtain in practical scenarios.
No doubt, some of these are strong arguments worthy enough to be taken seriously.
There is an extensive literature, which sometimes seems to overwhelm that of Bayesian inference itself, on
the advantages and disadvantages of Bayesian approaches. Bayesians’ contributions to this discussion have included defense (explaining how our methods reduce to classical methods as special cases, so that we can be as inoffensive as anybody if needed).
Obviously, Bayesian methods have filled many loopholes in classical statistical theory.
And always remember that you are subjected to mass-criticism only when you have done something truly remarkable walking against the tide of popular opinion.
Hence : "All Hail the iconoclasts of Statistical Theory:the Bayesians"
N.B. The above quote is mine XD
Wait for our next dose of Bayesian glorification!
Till then ,
Stay safe and cheers!
1."Critique of Bayesianism"- John D Norton
2."Bayesian Informal Logic and Fallacy" - Kevin Korb
3."Bayesian Analysis"- Gelman
4."Statistical Re-thinking"- Richard McElreath
One of the most controversial approaches to statistics, this post mainly deals with the fundamental objections to Bayesian methods and Bayesian school of thinking. Turning to the Bayesian crank, Fisher put forward a vehement objection towards Bayesian Inference, describing it as "fallacious rubbish".
However, ironically enough, it’s interesting to note that Fisher’s greatest statistical failure, fiducialism, was essentially an attempt to “enjoy the Bayesian omelette without breaking any Bayesian eggs" !
An inductive logic is a logic of evidential support. In a deductive logic, the premises of a valid deductive argument logically entail the conclusion, where logical entailment means that every logically possible state of affairs that makes the premises true must make the conclusion truth as well. Thus, the premises of a valid deductive argument provide total support for the conclusion. An inductive logic extends this idea to weaker arguments. In a good inductive argument, the truth of the premises provides some degree of support for the truth of the conclusion, where this degree-of-support might be measured via some numerical scale.
If a logic of good inductive arguments is to be of any real value, the measure of support it articulates should be up to the task. Presumably, the logic should at least satisfy the following condition:
Criterion of Adequacy (CoA):
The logic should make it likely (as a matter of logic) that as evidence accumulates, the total body of true evidence claims will eventually come to indicate, via the logic’s measure of support, that false hypotheses are probably false and that true hypotheses are probably true.
One practical example of an easy inductive inference is the following:
" Every bird in a random sample of 3200 birds is black. This strongly supports the following conclusion: All birds are black. "
This kind of argument is often called an induction by enumeration. It is closely related to the technique of statistical estimation.
Non-trivial calculi of inductive inference are shown to be incomplete. That is, it is impossible for a calculus of inductive inference to capture all inductive truths in some domain, no matter how large, without resorting to inductive content drawn from outside that domain. Hence inductive inference cannot be characterized merely as inference that conforms with some specified calculus.
A probabilistic logic of induction is unable to separate cleanly neutral support from disfavoring evidence (or ignorance from disbelief). Thus, the use of probabilistic representations may introduce spurious results stemming from its expressive inadequacy. That such spurious results arise in the Bayesian "doomsday argument" is shown by a re-analysis that employs fragments of inductive logic able to represent evidential neutrality. Further, the improper introduction of inductive probabilities is illustrated with the "self-sampling assumption."
While Bayesian analysis has enjoyed notable success with many particular problems of inductive inference, it is not the one true and universal logic of induction. Some of the reasons arise at the global level through the existence of competing systems of inductive logic. Others emerge through an examination of the individual assumptions that, when combined, form the Bayesian system: that there is a real valued magnitude that expresses evidential support, that it is additive and that its treatment of logical conjunction is such that Bayes' theorem ensues.
The fundamental objections to Bayesian methods are twofold: on one hand, Bayesian methods are presented as an automatic inference engine, and this raises suspicion in anyone with applied experience. The second objection to Bayes' comes from the opposite direction and addresses the subjective strand of Bayesian inference.
Andrew Gelman , a staunch Bayesian pens down an interesting criticism of the Bayesian ideology in the voice of a hypothetical anti-Bayesian statistician.
Here is the list of objections from a hypothetical or paradigmatic non-Bayesian ; and I quote:
"Bayesian inference is a coherent mathematical theory but I don’t trust it in scientific applications. Subjective prior distributions don’t transfer well from person to person, and there’s no good objective principle for choosing a non-informative prior (even if that concept were mathematically defined, which it’s not). Where do prior distributions
come from, anyway? I don’t trust them and I see no reason to recommend that other people do, just so that I can have the warm feeling of philosophical coherence. To put it another way, why should I believe your subjective prior? If I really believed it, then I could just feed you some data and ask you for your subjective posterior. That would save me a lot of effort!"
In 1986 , a statistician as prominent as Brad Efron restates these concerns mathematically:
"I like unbiased estimates and I like confidence intervals that really have their advertised confidence coverage. I know that these aren’t always going to be possible, but I think the right way forward is to get as close to these goals as possible and to develop robust methods that work with minimal assumptions. The Bayesian approach—to give up even trying to approximate unbiasedness and to instead rely on stronger and stronger assumptions—that seems like the wrong way to go. When the priors I see in practice are typically just convenient conjugate forms. What a coincidence that, of all the infinite variety of priors that could be chosen, it always seems to be the normal, gamma, beta, etc., that turn out to be the right choices?"
Well that really sums up every frequentist's rant about Bayes' 😀 !
Some frequentists believe that in the old days, Bayesian methods at least had the virtue of being mathematically
clean. Nowadays, they all seem to be computed using Markov chain Monte Carlo, which means that, not only can you not realistically evaluate the statistical properties of the method, you can’t even be sure it’s converged, just adding one more item to the list of unverifiable (and unverified) assumptions in Bayesian belief.
As the applied statistician Andrew Ehrenberg wrote :
" Bayesianism assumes:
(a) Either a weak or uniform prior, in which case why bother?,
(b) Or a strong prior, in which case why collect new data?,
(c) Or more realistically, something in between,in which case Bayesianism always seems to duck the issue."
Many are skeptical about the new found empirical approach of Bayesians which always seems to rely on the assumption of "exchangeability", which is almost impossible to obtain in practical scenarios.
No doubt, some of these are strong arguments worthy enough to be taken seriously.
There is an extensive literature, which sometimes seems to overwhelm that of Bayesian inference itself, on
the advantages and disadvantages of Bayesian approaches. Bayesians’ contributions to this discussion have included defense (explaining how our methods reduce to classical methods as special cases, so that we can be as inoffensive as anybody if needed).
Obviously, Bayesian methods have filled many loopholes in classical statistical theory.
And always remember that you are subjected to mass-criticism only when you have done something truly remarkable walking against the tide of popular opinion.
Hence : "All Hail the iconoclasts of Statistical Theory:the Bayesians"
N.B. The above quote is mine XD
Wait for our next dose of Bayesian glorification!
Till then ,
Stay safe and cheers!
1."Critique of Bayesianism"- John D Norton
2."Bayesian Informal Logic and Fallacy" - Kevin Korb
3."Bayesian Analysis"- Gelman
4."Statistical Re-thinking"- Richard McElreath
