12.5.5. Simple 1D Model Comparison with BAT#

This directory contains an example of using BAT to compare models for a 1D fit. Please see the examples in Simple 1D Fit with Bat to learn the basics of creating a such a model in BAT and how the binned likelihood is formulated.

To run this code, you will need to install BAT with the Cuba library. See the intructions in What is BAT? for how install BAT with Cuba.

The Data#

The data to be analyzed in this example is stored in a file in a directory above this one at: ../../example-data/fitme.root. In this ROOT file is a TTree (“BtoKstG”) with a branch named “B0_mbc”, that contains the beam-constrained masses of reconstructed B0 mesons. We will perform binned fits to this data. Nothing in this example is exclusive to bin fits, but for ease of presentation and speed, we used a binned fit.

The Models#

In all fits, we model the background as being distributed according to an ARGUS function

$\begin{split}P(x) \propto \left\{\begin{array}{lcccl} x \ \sqrt{k(x)} \ E^{-\frac{1}{2} \chi^2 k(x)} & & \mathrm{if} & & x \leq x_0\\ 0& & \mathrm{if} & & x > x_0.\end{array}\right.\end{split}$

where $$k(x) \equiv 1 - x / x_0$$.

We use three different models for the signal (described below). The signal and background components are each normalized over the range of the data. All functions are coded in Functions.h

Model: Crystal Ball#

In this model, the signal is distributed according to a Crystal Ball function

$\begin{split}P(x) \propto \left\{\begin{array}{lcccl} e^{-\frac{1}{2} [k(x)]^2} & & \mathrm{if} & & x \leq \alpha (k - \alpha) \leq 0\\ e^{-\frac{1}{2} \alpha^2} [1 + \frac{\alpha}{n}(k(x) - \alpha)]^{-n} & & \mathrm{if} & & \alpha (k - \alpha) > 0.\end{array}\right.\end{split}$

where $$k(x) \equiv (x - \mu) / \sigma$$. This function is normal at its mode, with a tail to one side (the direction of which depends on the sign of $$\alpha$$) that is a power distribution. This model has eight parameters: two yields ($$Y_S, Y_B$$); two parameters for the background ($$x_0, \chi$$); and four for the signal ($$\mu, \sigma, \alpha, n$$).

Mode: Limited Crystal Ball#

As we saw in the examples in Simple 1D Fit with Bat, the Crystal Ball function possibly has one unnecessary degree of freedom for our fit. In this model, this degree of freedom is removed and the signal is distributed according to a Crystal Ball function in the limit of large $$n$$:

$\begin{split}P(x) \propto \left\{\begin{array}{lcccl} e^{-\frac{1}{2} [k(x)]^2} & & \mathrm{if} & & x \leq \alpha (k - \alpha) \leq 0\\ e^{\frac{1}{2} \alpha^2} e^{-\alpha k(x)} & & \mathrm{if} & & \alpha (k - \alpha) > 0.\end{array}\right.\end{split}$

where $$k(x) \equiv (x - \mu) / \sigma$$. This function is normal at its mode, with an exponential tail to one side (the direction of which depends on the sign of $$\alpha$$). This model has seven parameters: two yields ($$Y_S, Y_B$$); two parameters for the background ($$x_0, \chi$$); and three for the signal ($$\mu, \sigma, \alpha$$).

Model: Double Normal#

In this model, the signal is distributed according to the (weighted) sum of two normal functions:

$P(x) \propto f \mathcal{N}(x | \mu_1, \sigma_1) + (1 - f) \mathcal{N}(x | \mu_2, \sigma_2).$

This model has nine parameters: two yields ($$Y_S, Y_B$$); two parameters for the background ($$x_0, \chi$$); and three for the signal ($$\mu_1, \sigma_1, \mu_2 - \mu_1, \sigma_2/\sigma_1, f$$).

Model Comparison#

In Bayesian statistics, one compares models by the ratio of their “evidences.” The evidence of a model is the integral of the likelihood times the prior:

$Z \equiv \int L(\mathrm{data}|\vec\lambda) \ P_0(\vec\lambda) d\vec\lambda$

This is the probability of the model to describe the data regardless of any specific values of parameters of the model. The integral is over all parameters of the model and over their entire allowed ranges. The Markov-chain algorithm of BAT cannot tell us the evidence. To integrate

When comparing two models, A and B, the ratio of evidences—known as the Bayes factor—tells you which model best explains the data:

$K_{AB} = Z_A / Z_B.$

If it is greater than 1, model A is more probable; if it is less than 1, model B is more probable. However, your personal belief in which model is best depends on your prior beliefs in the models. The ratio of your posterior beliefs—your beliefs after analyzing the data—is proportional to the ratio of your prior beliefs by the Bayes factor:

$P(A) / P(B) \equiv K_{AB} \times P_0(A) / P_0(B).$

When comparing several models, BAT calculates the probability the posterior probability for an individual model by

$P(A) \equiv Z_A\ P_0(A) / [Z_A\ P_0(A) + Z_B\ P_0(B) + Z_C\ P_0(C) + \dots];$

thus the sum of probabilities for each model is unity.

The Code#

To allow for easy generation of the models, we use one class Simple1DBinnedFit that takes a function in its constructor for calculating the signal component integrals.

The code in runSimple1DBinnedFit.cxx resembles that in ../Simple1Dfit/runSimple1DBinnedFit.cxx, exept now we use BCModelManager to store and marginalize our models.

Rather than defining our parameters in the constructors of our models, we define them in runSimple1DBinnedFix.cxx after instantiating them.

By calling BCModelManager::MarginalizeAll(), we marginalize all the models contained in the model manager. Then inside the loop that follows on the next lines, for each model, we improve the mode finding with Minuit (via FindMode()) and print the results of that model to a pdf file.

BCModelManager::Integrate() will integrate all the models, calculating their evidences, which we need to compare them. BAT has its own built-in methods to integrate models, but then can only handle the very simplest models; the simple models in this example are already too complicated. We rely on Cuba to integrate them. Be sure you have configured BAT with the --with-cuba=download option before installing it.

BCModelManager::PrintSummary() will print the summaries of each model it contains as well as a model comparison summary. The first block in the model-comparison summary is of the priors:

Summary :  - A priori probabilities:
Summary :  (0) p(Crystal Ball) = 1.000000
Summary :  (1) p(Limited Crystal Ball) = 1.000000
Summary :  (2) p(Double Normal) = 1.000000


We added the models without the additional argument for a prior belief, which defaults to unity, meaning all models have equal weight.

The second block contains the posteriors, calculated from both the evidences and the priors:

Summary :  - A posteriori probabilities:
Summary :  (0) p(Crystal Ball | data) = 0.028975
Summary :  (1) p(Limited Crystal Ball | data) = 0.970998
Summary :  (2) p(Double Normal | data) = 0.000026


We see that the limited Crystal-Ball function, with one fewer degree of freedom, is far more probable than the full Crystal-Ball function; this is Occam’s razor at work.

The final block of the output contains the Bayes factors for the individual comparisons:

Summary :  - Bayes factors:
Summary :      K := p(data | Crystal Ball) / p(data | Limited Crystal Ball) = 0.029841
Summary :      K := p(data | Crystal Ball) / p(data | Double Normal) = 1096.147862
Summary :      K := p(data | Limited Crystal Ball) / p(data | Double Normal) = 36733.255213


We see the limited Crystal-Ball function is greatly preferred to both the Crystal-Ball function and the double-normal function; and that the Crystal-Ball is greatly preferred over the double-normal.

This tells us more than we can learn from looking at just a $$\chi^2/\mathrm{ndf}$$ (as is given in Simple1DBinnedFit_comparison.pdf).

As with the examples in Simple 1D Fit with Bat, try uncommenting the blocks of code that set priors for the parameters—as we might do if we had information about them from fits to simulated data.

If you uncomment all prior-setting blocks, the difference in degrees of freedom between the models shrinks because now the parameters are guided by the priors. Think about what would happen if you fixed the parameters to values you got from fits to simulated data, instead of using priors: each parameter you fix removes a degree of freedom. Here the effect is somewhat the same, but more robust.

Having set priors, our posterior probabilities for the models completely invert:

Summary :  - A posteriori probabilities:
Summary :  (0) p(Crystal Ball | data) = 0.188235
Summary :  (1) p(Limited Crystal Ball | data) = 0.004994
Summary :  (2) p(Double Normal | data) = 0.806771


—the double-normal is now most probable and the limited crystal-ball is now least probable.