# Is the invariance property of the ML estimator nonsensical from a Bayesian perspective?

Casella and Berger state the invariance property of the ML estimator as follows:

However, it seems to me that they define the "likelihood" of $\eta$ in a completely ad hoc and nonsensical way:

If I apply basic rules of probability theory to the simple case wheter $\eta=\tau(\theta)=\theta^2$, I instead get the following: $$L(\eta|x)=p(x|\theta^2=\eta)=p(x|\theta = -\sqrt \eta \lor \theta = \sqrt \eta)=:p(x|A \lor B)$$ Now applying Bayes theorem, and then the fact that $A$ and $B$ are mutually exclusive so that we can apply the sum rule: $$p(x|A\lor B)=p(x)\frac {p(A\lor B|x)}{p(A\lor B)}=p(x|A\lor B)=p(x)\frac {p(A|x)+p(B|x)}{p(A)+p(B)}$$

Now applying Bayes' theorem to the terms in the numerator again: $$p(x)\frac {p(A)\frac {p(x|A)}{p(x)}+p(B)\frac {p(x|B)}{p(x)}}{p(A)+p(B)}=\frac {p(A)p(x|A)+p(B)p(x|B)}{p(A)+p(B)}$$

If we want to maximize this w.r.t to $\eta$ in order to get the maximum likelihood estimate of $\eta$, we have to maximize: $$p_\theta(-\sqrt \eta)p(x|\theta = -\sqrt \eta)+p_\theta(\sqrt \eta)p(x|\theta = \sqrt \eta)$$

Does Bayes strike again? Is Casella & Berger wrong? Or am I wrong?

• Possible duplicate of Invariance property of maximum likelihood estimator? Nov 15 '17 at 21:15
• The formal part after "If I apply basic rules of probability theory to the simple case wheter $\eta=\tau(\theta)=\theta^2$" does not change the question. The matter is fully covered in the excellent answer from Samuel Benidt. The likelihood values (and as a consequence the maximum) do not change due to the mapping. Yes you need to take special care if the mapping is not one-to-one. But that is a whole different issue than the changes occuring due to probability distributions when you apply a transform. Nov 15 '17 at 22:54
• I understand your frustration, Programmer2134 (& @MartijnWeterings). However, please be careful of your tone in your comments. Productive conversations are only possible when our be nice policy is followed. If you aren't interested in pursuing productive conversations, you need to post these questions elsewhere. Nov 16 '17 at 18:34
• @gung, You are completely right. And I regret reacting with that tone. I will stop doing it from now on. Sorry for this. Regarding the conversation, I am interested in pursuing productive ones, but felt that people's reactions in a couple of questions I asked were mostly counterproductive. Nevertheless, next time, I will respond differently. Nov 16 '17 at 19:20
• Thank you. It is best to assume people are responding in good faith. There are (relatively few, IMHO) occasions where people here aren't, but even then, sometimes they can be coaxed to come around. Nov 16 '17 at 19:26

As Xi'an says, the question is moot, but I think that many people are nevertheless led to consider the maximum-likelihood estimate from a Bayesian perspective because of a statement that appears in some literature and on the internet: "the maximum-likelihood estimate is a particular case of the Bayesian maximum a posteriori estimate, when the prior distribution is uniform".

I'd say that from a Bayesian perspective the maximum-likelihood estimator and its invariance property can make sense, but the role and meaning of estimators in Bayesian theory is very different from frequentist theory. And this particular estimator is usually not very sensible from a Bayesian perspective. Here's why. For simplicity let me consider a one-dimensional parameter and one-one transformations.

First of all two remarks:

1. It can be useful to consider a parameter as a quantity living on a generic manifold, on which we can choose different coordinate systems or measurement units. From this point of view a reparameterization is just a change of coordinates. For example, the temperature of the triple point of water is the same whether we express it as $$T=273.16$$ (K), $$t=0.01$$ (°C), $$\theta=32.01$$ (°F), or $$\eta=5.61$$ (a logarithmic scale). Our inferences and decisions should be invariant with respect to coordinate changes. Some coordinate systems may be more natural than others, though, of course.

2. Probabilities for continuous quantities always refer to intervals (more precisely, sets) of values of such quantities, never to particular values; although in singular cases we can consider sets containing one value only, for example. The probability-density notation $$\mathrm{p}(x)\,\mathrm{d}x$$, in Riemann-integral style, is telling us that
(a) we have chosen a coordinate system $$x$$ on the parameter manifold,
(b) this coordinate system allows us to speak of intervals of equal width,
(c) the probability that the value lies in a small interval $$\Delta x$$ is approximately $$\mathrm{p}(x)\,\Delta x$$, where $$x$$ is a point within the interval.
(Alternatively we can speak of a base Lebesgue measure $$\mathrm{d}x$$ and intervals of equal measure, but the essence is the same.)

Therefore, a statement like "$$\mathrm{p}(x_1) > \mathrm{p}(x_2)$$" does not mean that the probability for $$x_1$$ is larger than that for $$x_2$$, but that the probability that $$x$$ lies in a small interval around $$x_1$$ is larger than the probability that it lies in an interval of equal width around $$x_2$$. Such statement is coordinate-dependent.

Let's see the (frequentist) maximum-likelihood point of view
From this point of view, speaking about the probability for a parameter value $$x$$ is simply meaningless. Full stop. We'd like to know what the true parameter value is, and the value $$\tilde{x}$$ that gives highest probability to the data $$D$$ should intuitively be not too far off the mark: $$\tilde{x} := \arg\max_x \mathrm{p}(D \mid x)\tag{1}\label{ML}.$$ This is the maximum-likelihood estimator.

This estimator selects a point on the parameter manifold and therefore doesn't depend on any coordinate system. Stated otherwise: Each point on the parameter manifold is associated with a number: the probability for the data $$D$$; we're choosing the point that has the highest associated number. This choice does not require a coordinate system or base measure. It is for this reason that this estimator is parameterization invariant, and this property tells us that it is not a probability – as desired. This invariance remains if we consider more complex parameter transformations, and the profile likelihood mentioned by Xi'an makes complete sense from this perspective.

Let's see the Bayesian point of view
From this point of view it always makes sense to speak of the probability for a continuous parameter, if we are uncertain about it, conditional on data and other evidence $$D$$. We write this as $$\mathrm{p}(x \mid D)\,\mathrm{d}x \propto \mathrm{p}(D \mid x)\, \mathrm{p}(x)\,\mathrm{d}x.\tag{2}\label{PD}$$ As remarked at the beginning, this probability refers to intervals on the parameter manifold, not to single points.

Ideally we should report our uncertainty by specifying the full probability distribution $$\mathrm{p}(x \mid D)\,\mathrm{d}x$$ for the parameter. So the notion of estimator is secondary from a Bayesian perspective.

This notion appears when we must choose one point on the parameter manifold for some particular purpose or reason, even though the true point is unknown. This choice is the realm of decision theory [1], and the value chosen is the proper definition of "estimator" in Bayesian theory. Decision theory says that we must first introduce a utility function $$(P_0,P)\mapsto G(P_0; P)$$ which tells us how much we gain by choosing the point $$P_0$$ on the parameter manifold, when the true point is $$P$$ (alternatively, we can pessimistically speak of a loss function). This function will have a different expression in each coordinate system, e.g. $$(x_0,x)\mapsto G_x(x_0; x)$$, and $$(y_0,y)\mapsto G_y(y_0; y)$$; if the coordinate transformation is $$y=f(x)$$, the two expressions are related by $$G_x(x_0;x) = G_y[f(x_0); f(x)]$$ [2].

Let me stress at once that when we speak, say, of a quadratic utility function, we have implicitly chosen a particular coordinate system, usually a natural one for the parameter. In another coordinate system the expression for the utility function will generally not be quadratic, but it's still the same utility function on the parameter manifold.

The estimator $$\hat{P}$$ associated with a utility function $$G$$ is the point that maximizes the expected utility given our data $$D$$. In a coordinate system $$x$$, its coordinate is $$\hat{x} := \arg\max_{x_0} \int G_x(x_0; x)\, \mathrm{p}(x \mid D)\,\mathrm{d}x.\tag{3}\label{UF}$$ This definition is independent of coordinate changes: in new coordinates $$y=f(x)$$ the coordinate of the estimator is $$\hat{y}=f(\hat{x})$$. This follows from the coordinate-independence of $$G$$ and of the integral.

You see that this kind of invariance is a built-in property of Bayesian estimators.

Now we can ask: is there a utility function that leads to an estimator equal to the maximum-likelihood one? Since the maximum-likelihood estimator is invariant, such a function might exist. From this point of view, maximum-likelihood would be nonsensical from a Bayesian point of view if it were not invariant!

A utility function that in a particular coordinate system $$x$$ is equal to a Dirac delta, $$G_x(x_0; x) = \delta(x_0-x)$$, seems to do the job [3]. Equation $$\eqref{UF}$$ yields $$\hat{x} = \arg\max_{x} \mathrm{p}(x \mid D)$$, and if the prior in $$\eqref{PD}$$ is uniform in the coordinate $$x$$, we obtain the maximum-likelihood estimate $$\eqref{ML}$$. Alternatively we can consider a sequence of utility functions with increasingly smaller support, e.g. $$G_x(x_0; x) = 1$$ if $$\lvert x_0-x \rvert<\epsilon$$ and $$G_x(x_0; x) = 0$$ elsewhere, for $$\epsilon\to 0$$ [4].

So, yes, the maximum-likelihood estimator and its invariance can make sense from a Bayesian perspective, if we are mathematically generous and accept generalized functions. But the very meaning, role, and use of an estimator in a Bayesian perspective are completely different from those in a frequentist perspective.

Let me also add that there seem to be reservations in the literature about whether the utility function defined above makes mathematical sense [5]. In any case, the usefulness of such a utility function is rather limited: as Jaynes [3] points out, it means that "we care only about the chance of being exactly right; and, if we are wrong, we don't care how wrong we are".

Now consider the statement "maximum-likelihood is a special case of maximum-a-posteriori with a uniform prior". It's important to note what happens under a general change of coordinates $$y=f(x)$$:

1. the utility function above assumes a different expression: $$G_y(y_0;y) = \delta[f^{-1}(y_0)-f^{-1}(y)] \equiv \delta(y_0-y)\,\lvert f'[f^{-1}(y_0)]\rvert$$
2. the prior density in the coordinate $$y$$ is not uniform, owing to the Jacobian determinant;
3. the estimator is not the maximum of the posterior density in the $$y$$ coordinate, because the Dirac delta has acquired an extra multiplicative factor;
4. the estimator is still given by the maximum of the likelihood in the new, $$y$$ coordinates.
These changes combine so that the estimator point is still the same on the parameter manifold.

Thus, the statement above is implicitly assuming a special coordinate system. A tentative, more explicit statement would could be this: "the maximum-likelihood estimator is numerically equal to the Bayesian estimator that in some coordinate system has a delta utility function and a uniform prior".

The discussion above is informal, but can be made precise using measure theory and Stieltjes integration.

In the Bayesian literature we can find also a more informal notion of estimator: it's a number that somehow "summarizes" a probability distribution, especially when it's inconvenient or impossible to specify its full density $$\mathrm{p}(x \mid D)\,\mathrm{d}x$$; see e.g. Murphy [6] or MacKay [7]. This notion is usually detached from decision theory, and therefore may be coordinate-dependent or tacitly assumes a particular coordinate system. But in the decision-theoretic definition of estimator, something which is not invariant cannot be an estimator.

[1] For example, H. Raiffa, R. Schlaifer: Applied Statistical Decision Theory (Wiley 2000).
[2] Y. Choquet-Bruhat, C. DeWitt-Morette, M. Dillard-Bleick: Analysis, Manifolds and Physics. Part I: Basics (Elsevier 1996), or any other good book on differential geometry.
[3] E. T. Jaynes: Probability Theory: The Logic of Science (Cambridge University Press 2003), §13.10.
[4] J.-M. Bernardo, A. F. Smith: Bayesian Theory (Wiley 2000), §5.1.5.
[5] I. H. Jermyn: Invariant Bayesian estimation on manifolds https://doi.org/10.1214/009053604000001273; R. Bassett, J. Deride: Maximum a posteriori estimators as a limit of Bayes estimators https://doi.org/10.1007/s10107-018-1241-0.
[6] K. P. Murphy: Machine Learning: A Probabilistic Perspective (MIT Press 2012), especially chap. 5.
[7] D. J. C. MacKay: Information Theory, Inference, and Learning Algorithms (Cambridge University Press 2003), http://www.inference.phy.cam.ac.uk/mackay/itila/.

• There exist ways to define invariant Bayes estimators, in the above sense, by creating a functional loss function, as eg the Kullback-Leibler divergence between two densities. I called these losses intrinsic losses in a 1996 paper. Aug 23 '18 at 8:50

From a non-Bayesian view point, there is no definition of quantities like $$p(x|\theta = -\sqrt \eta \lor \theta = \sqrt \eta)$$ because $\theta$ is then a fixed parameter and the conditioning notation does not make sense. The alternative you propose relies on a prior distribution, which is precisely what an approach such as the one proposed by Casella and Berger wants to avoid. You can check the keyword profile likelihood for more entries. (And there is no meaning of right or wrong there.)

• How does this contradict what I'm saying? My point was that it is nonsensical from a bayesian perspective. The problem I have with Casella and Berger's solution, is that basically, they come up with a totally new ad-hoc definition of likelihood, in such a way that their desired conclusion is reached. If one would make a consistent definition of likelihood, namely the one I gave above, then the conclusion would be different. Of course Casella and Berger may want to avoid bringing in priors, but the only way to do so is to come up with an ad hoc change of definition of likelihood. Nov 14 '17 at 12:25
• If you want to keep a Bayesian perspective, the question is moot since most non-Bayesian results will not make sense or be "consistent" with Bayesian principles. Nov 14 '17 at 12:34