Difference between revisions of "Priors"

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In the context of [[Bayes's Theorem]], '''Priors''' refer generically to the beliefs an agent holds regarding a fact, hypothesis or consequence, before being presented with evidence.  More technically, in order for this agent to calculate a posterior probability using Bayes's Theorem, this referred prior probability and [[likelihood distribution]] are needed.
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{{wikilink|Prior probability}}
  
==Examples==
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In the context of [[Bayes's Theorem]], '''Priors''' refer generically to the beliefs an agent holds regarding a fact, hypothesis or consequence, before being presented with evidence. Upon being presented with new evidence, the agent can multiply their prior with a [[likelihood distribution]] to calculate a new (posterior) probability for their belief.
  
Suppose you had a barrel containing some number of red and white balls.  You start with the belief that each ball was independently assigned red color (vs. white color) at some fixed probability. Furthermore, you start out ignorant of this fixed probability (the parameter could be anywhere between 0 and 1). Each red ball you see then makes it ''more'' likely that the next ball will be red (following a [http://en.wikipedia.org/wiki/Rule_of_succession Laplacian Rule of Sucession]).
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Suppose you had a barrel containing some number of red and white balls.  You start with the belief that each ball was independently assigned red color (vs. white color) at some fixed probability. Furthermore, you start out ignorant of this fixed probability (the parameter could be anywhere between 0 and 1). Each red ball you see then makes it ''more'' likely that the next ball will be red, following a [http://en.wikipedia.org/wiki/Rule_of_succession Laplacian Rule of Succession]. For example, seeing 6 red balls out of 10 suggests that the initial probability used for assigning the balls a red color was .6, and that there's also a probability of .6 for the next ball being red.
  
 
On the other hand, if you start out with the prior belief that the barrel contains exactly 10 red balls and 10 white balls, then each red ball you see makes it ''less'' likely that the next ball will be red (because there are fewer red balls remaining).
 
On the other hand, if you start out with the prior belief that the barrel contains exactly 10 red balls and 10 white balls, then each red ball you see makes it ''less'' likely that the next ball will be red (because there are fewer red balls remaining).
  
Thus our prior can affect how we interpret the evidence.  The first prior is an inductive prior - things that happened before are predicted to happen again with greater probability.  The second prior is anti-inductive - the more red balls we see, the fewer we expect to see in the future.
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Thus our prior affects how we interpret the evidence.  The first prior is an inductive prior - things that happened before are predicted to happen again with greater probability.  The second prior is anti-inductive - the more red balls we see, the fewer we expect to see in the future.
  
In both cases, you started out believing something about the barrel - presumably because someone else told you, or because you saw it with your own eyes. But then their words, or even your own eyesight, was evidence, and you must have had prior beliefs about probabilities and likelihoods in order to interpret the evidence. So it seems that an ideal Bayesian would need some sort of inductive prior at the very moment they were born. Where an ideal Bayesian would get this prior, has occasionally been a matter of considerable controversy in the philosophy of probability.
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As a real life example, consider two leaders from different political parties. Each one has his own beliefs - priors - about social organization and the roles of people and government in society. These differences in priors can be attributed to a wide range of factors, ranging from their educational backgrounds to hereditary differences in personality. However, neither can show that his beliefs are better than those of the other, unless he can show that his priors were generated by sources which track reality better<ref>Robin Hanson (2006). "Uncommon Priors Require Origin Disputes". Theory and Decision 61 (4) 319–328. http://hanson.gmu.edu/prior.pdf</ref>.
  
As a real life example, consider two leaders from different political parties. Each one has his own beliefs about social organization and the roles of people and government in society. These differences can be attributed to a wide range of factors, from genetic variability to education influence in their personalities and condition the politics and laws they want to implement. However, neither can show that his beliefs are better than those of the other, unless he can show that his priors were generated by sources which track reality better<ref>Robin Hanson (2006). "Uncommon Priors Require Origin Disputes". Theory and Decision 61 (4) 319–328. http://hanson.gmu.edu/prior.pdf</ref>.
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Because carrying out any reasoning at all seems to require a prior of some kind, ideal Bayesians would need some sort of priors from the moment that they were born. The question of where an ideal Bayesian would get this prior from has occasionally been a matter of considerable controversy in the philosophy of probability.
  
 
==Updating prior probabilities==
 
==Updating prior probabilities==
It's important to notice that piors represent a commitment to a certain belief. That is, as seen in this [http://lesswrong.com/lw/ear/whats_the_value_of_information/7anb Less Wrong discussion], you can't ''shift'' your prior. What happens is that, after being presented with the evidence, you update your prior probability, thus actually becoming a posterior probability.
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In informal discussion, people often talk about "updating" their priors. This is technically incorrect, as one does not change their prior probability, but rather uses it to calculate a posterior probability. However, as this posterior probability then becomes the prior probability for the next inference, talking about "updating one's priors" is often a convenient shorthand.
 
 
It should be noticed, however, that it can make sense to informally talk about updating priors when dealing with a sequence of inferences. In such cases, posterior probability happens to become a prior for the next inference, so it can make it easier to refer to it in that way.
 
  
 
==References==
 
==References==

Revision as of 17:53, 27 October 2012

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In the context of Bayes's Theorem, Priors refer generically to the beliefs an agent holds regarding a fact, hypothesis or consequence, before being presented with evidence. Upon being presented with new evidence, the agent can multiply their prior with a likelihood distribution to calculate a new (posterior) probability for their belief.

Suppose you had a barrel containing some number of red and white balls. You start with the belief that each ball was independently assigned red color (vs. white color) at some fixed probability. Furthermore, you start out ignorant of this fixed probability (the parameter could be anywhere between 0 and 1). Each red ball you see then makes it more likely that the next ball will be red, following a Laplacian Rule of Succession. For example, seeing 6 red balls out of 10 suggests that the initial probability used for assigning the balls a red color was .6, and that there's also a probability of .6 for the next ball being red.

On the other hand, if you start out with the prior belief that the barrel contains exactly 10 red balls and 10 white balls, then each red ball you see makes it less likely that the next ball will be red (because there are fewer red balls remaining).

Thus our prior affects how we interpret the evidence. The first prior is an inductive prior - things that happened before are predicted to happen again with greater probability. The second prior is anti-inductive - the more red balls we see, the fewer we expect to see in the future.

As a real life example, consider two leaders from different political parties. Each one has his own beliefs - priors - about social organization and the roles of people and government in society. These differences in priors can be attributed to a wide range of factors, ranging from their educational backgrounds to hereditary differences in personality. However, neither can show that his beliefs are better than those of the other, unless he can show that his priors were generated by sources which track reality better[1].

Because carrying out any reasoning at all seems to require a prior of some kind, ideal Bayesians would need some sort of priors from the moment that they were born. The question of where an ideal Bayesian would get this prior from has occasionally been a matter of considerable controversy in the philosophy of probability.

Updating prior probabilities

In informal discussion, people often talk about "updating" their priors. This is technically incorrect, as one does not change their prior probability, but rather uses it to calculate a posterior probability. However, as this posterior probability then becomes the prior probability for the next inference, talking about "updating one's priors" is often a convenient shorthand.

References

  1. Robin Hanson (2006). "Uncommon Priors Require Origin Disputes". Theory and Decision 61 (4) 319–328. http://hanson.gmu.edu/prior.pdf

Blog posts

See also