In an earlier post, I described how the method of maximum likelihood can deviate
radically from the more logical results obtained from Bayes’ theorem, when
there is strong prior information available. At the end of that post, I
promised to describe another situation where maximum likelihood can fail to
capture the information content of a problem, even when the prior distribution for the competing hypotheses is relatively uninformative. Having introduced the foundations of
Bayesian model comparison, here, I can now proceed to describe that
situation. I’ll follow closely a line of thought developed by Jaynes, in the relevant chapter
from ‘Probability theory: the logic of science.’

In parameter
estimation we have a list of model parameters, θ, a dataset, D, a model M, and general information, I, from
which we formulate

(1) |

P(D | θMI) is
termed the likelihood function, L(θ | MDI) or more briefly, L(θ). The maximum
likelihood estimate for θ is denoted.

When we have a
number of alternative models, denoted by different subscripts, the problem of
assessing the probability that the j

^{th}model is the right one is just an equivalent one to that of parameter estimation, but carried out at a higher level:(2) |

The likelihood
function here makes no assumption about the set of fitting parameters used, and
so it decomposes into an integral over the entire available parameter space (extended sum rule, with P(H

_{i}H_{j}) = 0, for all i ≠ j):(3) |

and the denominator is as usual just the summation over all

*n*competing models, and so we get

In orthodox hypothesis testing, the weight that a hypothesis is to be given (P(H | DI) for us) is addressed with a surrogate measure: the likelihood function, which is what we would write as P(D | HI). Parameter estimation has never been considered in orthodox statistics to be remotely connected with hypothesis testing, but, since we now recognize that they are really the same problem, we can see that if the merits of two models, M

_{j}and M

_{k}are to be compared, orthodox statistics should do so by calculating the ratio of the likelihood functions at their points of maximum likelihood:

This, I admit,
is something of a straw-man metric – I’m not aware if anybody actually uses this
ratio for model comparison, but if we accept the logic of maximum likelihood,
then we should accept the logic of this method. (Non-Bayesian methods that attempt to penalize a model for having excessive
degrees of freedom, such as the adjusted correlation coefficient, exist, but seem to me to
require additional

*ad hoc*principles to be introduced into orthodox probability theory.) Let’s see, then, how the ratio in expression (5) compares to the Bayesian version of Ockham’s razor.
In calculating
the ratio of the probabilities for two different models, the denominators will
cancel out – it is the same for all models.

Lets express
the numerator in a new form by defining the Ockham factor, W (for William, I guess), as follows:

which is the
same as

from which it
is clear that

Now we can
write the ratio of the probabilities associated with the two models, M

_{j}and M_{k}, in terms of expression (5):
So the
orthodox estimate is multiplied by two additional factors: the prior odds ratio
and the ratio of the Ockham factors. Both of these can have strong impacts on the result.
The prior odds for the hypotheses under investigation can be important when we already had reason to prefer one model
to the other. In parameter estimation, we’ll
often see that these priors make negligible difference in cases where the data carry a lot of information. In model comparison, however, prior information of another kind can
have enormous impact, even when the data are extremely informative. This is
introduced by the means of the Ockham factor, which can easily overrule both
the prior odds and the maximum-likelihood ratio.

We can
visualize this in one dimension, by approximating the sharply peaked function
below by the adjacent square pulse, with the same area:

Since the
prior probability density P(θ | MI) varies slowly over the region of maximum likelihood, we observe that

which
indicates that the Ockham factor is a measure of the amount of prior
probability for θ that is packed into the high-likelihood region centered on , which has been singled out by the data. We can now
make more concrete how this amount of prior probability in the high likelihood
region relates to the ‘simplicity’ of a model by noting again the consequences
of augmenting a model with an additional free parameter. This additional
parameter adds another dimension to the parameter space, which, by virtue of
the fact that the total probability density must be normalized to unity,
necessitates a reduction of magnitude of the peak of P(θ | MI), compared to the low-dimensional model. Lets
assume that the more complex model is the same as the simple one, but with some
additional terms patched in (the parameter sample space for the smaller model is
embedded in that of the larger model).
Then we see that it is only if the maximum-likelihood region for the
more complex model is far from that of the simpler model, that the Ockham
factor has a chance to favor the more complex model.

For me, model comparison is closest to the heart of science. Ultimately, a scientist does not care much about the magnitude of some measurement error, or trivia such as the precise amount of time it takes the Earth to orbit the sun. The scientist is really much more interested in the nature of the cause and effect relationships that shape reality. Many scientists are motivated by desire to understand why nature looks the way it does, and why, for example, the universe supports the existence of beings capable of pondering such things. Model comparison lifts statistical inference beyond the dry realm of parameter estimation, to a wonderful place where we can ask: what is going on here? A formal understanding of model comparison (and a resulting intuitive appreciation) should, in my view, be something in the toolbox of every scientist, yet it is something that I have stumbled upon, much to my delight, almost by chance.

Great site and articles. Correct me if I'm wrong, but I believe that the model prior term, p(M|I), is missing in the summation in the denominator of equation 4 above.

ReplyDeleteIncidentally, you might be interested in the following paper, published in the journal Science and Education, which examines theism (and supernatural beliefs in general) from a Bayesian perspective.

Can Science Test Supernatural Worldviews?

by Yonatan Fishman

Links:

http://www.springerlink.com/content/g43043g3x2t53n56/

http://www.naturalism.org/Can%20Science%20Test%20Supernatural%20Worldviews-%20Final%20Author's%20Copy%20(Fishman%202007).pdf

Cheers!

Thanks - disappointed that that one slipped past. Well spotted, the error is now fixed. Another invitation to join the Bayesian Conspiracy on its way soon, I think (but please, remember to keep it quiet).

DeleteThat looks like it might be an interesting paper, I'll have a look as soon as I get a chance. My own feeling is that 'supernatural' is an empty concept, but regardless of what one takes the word to mean, the important point is that if we can measure it, then science rules, if we can't measure it, then discussing it has no value. Thanks for the link, I'm interested to see the argument.