Posterior distribution for the number of time correlation decay channels for a polymer solution of polyethylene glycol with a molecular weight of

## Abstract

The rapidly improving performance of inelastic scattering instruments has prompted tremendous advances in our knowledge of the high-frequency dynamics of disordered systems, yet also imposing new demands to the data analysis and interpretation. This ongoing effort is likely to reach soon an impasse, unless new protocols are developed in the data modeling. This need stems from the increasingly detailed information sought for in typical line shape measurements, which often touches or crosses the boundaries imposed by the limited experimental accuracy. Given this scenario, the risk of a bias and an over-parametrized data modeling represents a concrete threat for further advances in the field. Being aware of the severity of the problem, we illustrate here the new hopes brought in this area by Bayesian inference methods. Making reference to recent literature results, we demonstrate the superior ability of these methods in providing a probabilistic and evidence-based modeling of experimental data. Most importantly, this approach can enable hypothesis test involving competitive line shape models and is intrinsically equipped with natural antidotes against the risk of over-parametrization as it naturally enforces the Occam maximum parsimony principle, which favors intrinsically simple models over overly complex ones.

### Keywords

- inelastic X-ray scattering
- inelastic neutron scattering
- Bayes analysis
- MCMC methods
- model choice

## 1. Introduction

In the last decade, a large amount of inelastic neutron and X-ray scattering measurements focused on the study of the collective atomic dynamics of disordered system [1, 2, 3, 4, 5]. Although, across the years, the analysis of the line shape reported in these measurements seldom benefited from the support of a Bayesian inference analysis, the need of this statistical tool is becoming increasingly urgent. As a general premise, it is worth stressing that a scattering measurement somehow resembles a microscope pointed on the dynamics, whose “focus” can be adjusted by suitable choice of the momentum

One could guess that such a sharp spectral shape does not leave any room for interpretative doubts, also considering that the limiting hydrodynamic spectral profile is exactly known as analytically treatable starting from the application of mass, momentum, and energy conservation laws. Although these statements appear partly true, the very concept of “interpretative doubt” sounds grossly ill-defined before spelling out explicitly the accuracy required to the interpretation one alludes to. Despite its pioneering nature, the quality of the measurements in panel A seems certainly adequate for a precise determination of the side-peak position, probably not much so for a detailed analysis of the spectral tails, which are dominated by the slowly decaying resolution wings. Nonetheless such a shape might still appear a more encouraging candidate for a line shape analysis than its counterpart reported in panel B of Figure 1 which is featured by broad and loosely resolved spectral features, besides a definitely poorer count statistics. Given that the latter result is fairly prototypical of terahertz spectroscopic measurements on simple disordered systems, one might wonder why, thus far, the analysis of these measurements failed to benefit from Bayesian inference methods as routine line shape analysis tools. Aside of hardly justifiable initial mistrusts, a likely explanation is that only recently these spectroscopic techniques transitioned to a mature age in which the very detection of collective modes in amorphous systems can no longer be considered a discovery in itself, and detailed analyses of the spectral shape are more and more common and required. Again, the take-on message of this course of events is that the pivotal issue is the adequacy of a given measurement to provide the sought for information, rather than the quality of the measurement in itself. The unbalance between an unavoidably limited experimental performance and the rapidly increasing interpretative needs dramatically enhances the risk of “good faith overinterpretations” representing a lethal threat for the progress of knowledge.

**Listen, the data talk!** Every time we need to proceed with a data analysis, we could be induced or even tempted, on the basis of our prior knowledge or intuition, to somehow suggest the data what they should tell us about the properties of the system we are investigating. Being driven by acquired knowledge is not necessarily a wrong attitude, it is actually a natural demeanor which effectively drives the cognitive process and the progress of knowledge. However it could become deceiving if we do not have well-consolidated insight about the system under investigation and the observed data are not accurate enough or barely informative. In such cases, in fact, it is highly probable that we just adapt a model to the data, which fits them as well as many other possible models, with the only advantage to deliver results and solutions we feel more at ease with, as they confirm our prior beliefs. This model, of course, can be really plausible and reasonably pondered, and the solution adopted can accidentally be the right one; however, it would be desirable a robust method to quantify how much we can trust such a solution, either in itself or in comparison with alternative ones. We surely want to avoid an aprioristic reliance in a model, which might coerce data to confirm certain results preventing them from providing new insights on the investigated system.

When dealing with neutron or X-ray scattering, the statistical accuracy of spectral acquisition is the primary concern. For the most varied reasons, e.g., relating to the scattering properties of the sample, the integration time, or the count rate of the measurement, the achieved count statistics may either be adequate for a rigorous data analysis or, as often happens, not as good as we would like it to be. In the latter case, the experimental data might not be accurate enough to tell us everything about the physical problem under scrutiny. They could tell us something, but not everything! This is why we need a solid inferential method capable of extracting the maximum amount of information from the data acquired and possibly providing us with a quantitative probabilistic evaluation of the different models that are compatible with the data at hand. Especially when nothing or very little is known about a specific sample or system, the point is, given the observed data, how plausible is a specific model? What is the precision of the conclusions drawn from this model? Are there other possible interpretations of the data at hand? To what extent are different models and interpretations supported by the observed data?

A Bayesian inferential approach provides answers to all these questions on a probabilistic basis, along with a sound criterion to integrate any prior knowledge in the process of data analysis. Bayesian inference, in fact, recognizes the importance of including prior knowledge in the analysis. When we do have well-established prior knowledge about a sample property or a general law a physical phenomenon must comply with, it would be insane and pointless not to use this information. Such a prior knowledge, in fact, can protect us from the risk of making mistakes in the description of experimental data, hence in their interpretation. In the Bayesian framework, prior knowledge takes the form of probability statements so that different probabilities, ranging from zero to one, can be attributed to competitive explanations of the data. In this way, less probable explanations are not excluded a priori but simply given a smaller prior probability. The a priori probability of different explanations is then updated, through the Bayes theorem, based on the new information provided by the data. The results of this analysis, thus, assume the form of posterior probabilities. On this basis, one can easily establish which model is most supported by both data and prior knowledge, what are the posterior probabilities of alternative models and those of their parameters, and which provides a ground to appreciate the precision of their estimates. In addition, Bayesian methods naturally embody the Occam’s razor principle, thus favoring simpler models over unnecessarily complex ones. Last but not least, Bayesian estimation algorithms are generally less affected by the presence of local optima in the parameter space and are not sensitive to the starting values used to initialize the estimation process.

The aim of this chapter is to illustrate how Bayesian inference can be used in X-ray and neutron scattering applications. The Bayesian approach proposed here is implemented through an estimation algorithm, which makes use of Markov chains Monte Carlo (MCMC) methods [9, 10] integrated, where appropriate, with a reversible jump (

## 2. An example: searching for differences

Depending on the problem at hand, our approach to data analysis can be very different. Imagine that we want, as a toy or teaching example, to measure either the neutron or the X-ray scattering spectrum from a system whose spectrum is well-known and its interpretation unanimously agreed. For instance, we aim at extracting the phonon dispersion curve from the thoroughly measured spectral density

More often, we face a different problem, as we want to measure for the first time a certain system on which we might not have previous knowledge. Alternatively, we could have prior knowledge about that same system, yet in different thermodynamic or environmental conditions— for instance, a liquid either in bulk or confinement geometries—and possible effects of these peculiar conditions are under scrutiny. Changes could also be very small, and, since detecting them is the focus of our research, it is essential to take the most impartial and noninvasive approach. In this second situation, it would be desirable not to rely too heavily on previous results when choosing the model and to allow the measurement to reveal possible new features.

The two situations mentioned above notably differ in the amount of information available on the system before analyzing the data. In the first case, we have a complete knowledge of the system, while, in the second case, this knowledge is partial or even lacking at all. In this second situation, a traditional approach would consist in either best fitting a model we deem adequate for the data, e.g., well-assessed for the considered sample, albeit only in different thermodynamic or environmental conditions, or fitting competing models to establish the one best performing based on criteria agreed upon, e.g., the chi-square value. Following the first path, we hinge too much on a specific model and on previous partial knowledge, thus jeopardizing the chance of new findings. On the other hand, the second path would be less coercive at the cost of completely ignoring previous partial knowledge. In addition, the model chosen would be simply the one providing the best fit, but no assessment can be made on the plausibility of this or any other fitting model, based on the data measured. Conversely, a Bayesian approach to data analysis would, instead, allow to assign a different prior probability to the different models (accounting for the uncertainty of available information on the system) and, then, revise these probabilities in the light of the data to deliver the posterior probability of each model, conditional on the data at hand.

## 3. Bayesian inference

### 3.1 The Bayes theorem

The Bayes theorem stems from the theorem of compound probability and from the definition of conditional probability. If we consider two events

where

From Eq. (1), we immediately get:

which is nothing else than the Bayes theorem.

Let us now consider

where

### 3.2 The prior distribution

Let us consider the different elements of Eq. (4), starting with the prior distribution (or simply prior) *elicited* for the parameters, given the information at our disposal *before* data collection. Using a slightly more redundant notation, the prior can be explicitly denoted as

Just to make a few examples, it might be possible that a certain parameter

In other situations, the information available on the parameters might be more vague. For example, we might simply know that a certain parameter must be nonnegative or that it must range in a limited interval, as often the case of neutron scattering hampered by severe kinematic constraints. Nonnegative parameters can be a priori assumed to follow, for example, a truncated Gaussian or a gamma distribution, and, if no other information is available, the prior distribution will be adjusted to make allowance for a large parameter variability, reflecting the noninformative initial guess. Parameters having random or hardly quantifiable variations within limited windows can be assumed to approximately follow a uniform distribution over such a window. Also, whenever feasible, any mutual entanglement between parameters, as well as any selection, conservation, or sum rule, should be embodied in a usable distribution function complementing our prior knowledge

Notice that, even if it is common practice to assume that the parameters are a priori independently distributed, correlation between them can be naturally induced by the data, through the combination of the likelihood and the prior. Parameters can be a posteriori correlated, even if they are a priori independent.

### 3.3 The likelihood function

The likelihood function is the joint probability of the observed data, conditional on the model adopted and its parameter values. Notice that for continuous data, the likelihood becomes a density of probability. Let *i*-th result is to be considered a random variable

The left side of Eq. (6) is the likelihood function for the observed sample

To be more specific, we can consider spectroscopic data. The observable directly accessed by a spectroscopic measurement is the spectrum of the correlation function of density fluctuation, or dynamic structure factor

where

Under the assumption above, the likelihood function is:

Conditional on a certain value of the parameter vector

### 3.4 The posterior distribution and its normalizing constant

The term on the left-hand side of Eq. (3) is the joint posterior distribution of the model parameters, given prior knowledge and measured data, i.e., *after* data collection. It incorporates both prior knowledge and the information conveyed by the data, and Bayesian inference completely relies on it. In practice, prior knowledge about the investigated problem is modified by the data evidence (through the likelihood function) to provide the final posterior distribution ( Figure 2 ). Estimates for a single parameter

The term in the denominator of Eq. (3):

is generally called the marginal likelihood and represents the probability of observing the measured data

For this reason, Bayesian inference usually needs to resort to MCMC methods to simulate the joint posterior distribution. MCMC algorithms, in fact, allow to draw values from distributions known up to a normalizing constant, as is often the case for

To illustrate an interesting point, let us go back to the example considered before, in which we want to analyze spectroscopic data that can be modeled as in Eq. (7) and for which the likelihood is given in Eq. (8). Imagine to have no prior information at all on the parameters of the model so that the only sensible choice for the prior is a uniform distribution on the parameter space. Then, from Eqs. (8) and (10), it follows that:

which implies that the posterior distribution is a multivariate Gaussian. As already mentioned, parameters can be estimated taking the mean of the posterior distribution, which, for a Gaussian distribution, corresponds to the median, mode, and maximum of the distribution. Therefore Bayesian parameter estimates are obtained as those values of

Despite the asymptotic equivalence, sometimes parameters are much easier estimated in a Bayesian rather than in a frequentist perspective. Frequentist estimation, in fact, is generally based on least squares or maximum likelihood methods, and this might be a problem in the presence of local optima. If, for example, the starting values of the parameters, needed to initialize the optimization algorithm, are close to a local optimum, the algorithm might be trapped in this suboptimal solution. As a consequence, different starting values might determine different solutions and, thus, parameter estimates. The Bayesian estimate of a parameter, as stated before, is instead obtained as the mean of its posterior distribution, marginalized with respect to all other parameters. This estimation procedure does not involve any minimization or maximization, and, thus, the fitting algorithm does not risk to get trapped in local optima, and the results are independent from starting values used in the MCMC algorithm used to simulate the posterior distribution (see Section 3.6). It might happen, obviously, that the posterior of one or more parameters is bimodal or multimodal. The presence of different parameter regions with high posterior density might suggest that the data show some evidence in favor of a more complex model but not enough for this model to have the highest posterior probability. In this case, it is not reasonable to use the mean as a point estimate for the parameters, since it might fall in a low posterior density region, and the mode of the posterior distribution can be used in its place. In such situations of posterior multimodality, it is evident how the whole posterior distribution conveys a much richer information than the simple parameter estimate.

### 3.5 The Occam’s razor principle

Even if Bayesian and classical analysis asymptotically give the same results, Bayesian results always have a probabilistic interpretation, and this is particularly relevant when we need to compare different models and determine, for instance, the number of spectral excitations (in the frequency domain) or the number of relaxations (in the time domain). In addition, the Bayesian method represents a natural implementation of the Occam’s razor [18, 19, 20]: this principle is intrinsic to Bayesian inference and is a simple consequence of the adoption of the Bayes theorem. In model choice problems, in fact, the posterior probabilities of the different models naturally penalize complex solutions with respect to simple ones, thus conforming to the parsimony principle.

To see this, consider Eq. (4), and imagine that the parameter vector also includes a model indicator parameter

Now, consider for simplicity just two possible models, the first one, denoted as

### 3.6 Bayesian computation of model parameters

As already stated, Bayesian inference completely relies on the joint posterior distribution *candidate* value *proposal* distribution

where

Concerning the proposal distribution, this should be chosen as a distribution from which it is easy to sample. It could be, for instance, a normal distribution centered on the current value of the parameter and with a certain variance which can be adjusted and used as a tuning parameter. This locution alludes to the circumstance that adjustments of this parameter can literally tune the step of the parameter updates. For a normal proposal distribution, a large variance allows the new value

When the parameter vector also includes a model indicator parameter, a further move needs to be considered to update this parameter and to allow the algorithm to explore different models. This move is a reversible jump [11] step, which is specifically designed to allow the Markov chain to move between states having different dimensions (since the dimension of the parameter space varies accordingly to the model considered).

As a final remark, consider that when the MCMC algorithm reaches convergence, after a so called “burn-in” period, the draws not only effectively represent samples from the joint posterior distribution but are also theoretically independent from the starting values of each parameter. Few examples about this point are shown in Table 1 of Ref. [12]. Notice, however, that the time required to reach convergence might vary a lot depending on the data and the prior. For example, peaked unimodal posterior distributions (i.e., highly informative data) generally speed up convergence, as well as the availability of an important prior information, which reduces the size of the effectively accessible parameter space. On the contrary, the presence of many high posterior density regions can hinder and slow down convergence.

k | P(k|y)% |
---|---|

1 | 8.47 |

2 | 61.83 |

3 | 23.91 |

4 | 4.41 |

5 | 1.12 |

6 | 0.26 |

## 4. The Bayesian approach in neutron and X-ray scattering spectroscopy

### 4.1 Neutron and X-ray Brillouin scattering

One of the models commonly used to analyze either neutron or X-ray scattering data is the so-called damped harmonic oscillator (DHO) profile, which we report here below:

where *j*-th DHO is characterized by its undamped oscillation frequency

To fit the experimental data, the model in Eq. (14) needs to be convoluted with the instrument resolution, and it can conceivably sit on top of an unknown linear background. Overall, the final model used to approximate the measured line shape is given by:

where

For IXS, Eqs. (14–16) are still formally valid although the instrument resolution function has usually a slightly more complex shape which appears in the convolution of Eq. (15) either as approximated by an analytical model or measured from the signal of an almost elastic scatterer; obviously, in the latter case, the convolution is computed numerically. The final model is further corrupted by an additive Gaussian noise, having a variance that, for instance, can be taken proportional to each data point. Thus, the experimental data points are given by:

with

where

The whole parameter vector for the model in Eq. (17) is

Bayesian inference is, then, based on the joint posterior of the whole parameter vector

Once the convergence is attained, after a burn-in period, at each sweep

from the joint posterior

where each row is a particular draw of the whole parameter vector _{……..}4,5,4,3, 4, 3, 2_{….}). Therefore, the posterior probability that the number of modes is equal to a specific value

Once a particular model with, let us say,

In assessing convergence, a valuable tool is provided by trace plots, which show the sampled values of a parameter over the sweeps of the algorithm. Ideally, a trace plot should exhibit rapid up-and-down variation with no long-term trends or drifts. Imagining to break up this plot into a few horizontal sections, the trace within any section should not look much different from the trace in any other section. This indicates that the algorithm has converged to the posterior distribution of the parameters. Other convergence criteria can be found, for example, in Ref. [23]. Figure 4 shows the trace plots of three DHO-mode frequencies

In Figure 5 , we report an example of Bayesian analysis applied to neutron Brillouin scattering data from liquid gold [12] at different values of the momentum transfer

Even in this straightforward case, however, additional insights can be obtained from the posterior distributions delivered by the Bayesian inference. For example, in Figure 6 , it can be noticed that, as the value of

To investigate these issues, one can look, for example, at the posterior distributions for the excitation frequency

The shape of these posterior distributions provides a measure of the precision with which the parameter is estimated. For example, ^{−1}), the oscillation mode becomes so highly damped that it can be fitted equally well either by two distinct DHO peaks or by a (broader) single one in the middle of the two. At this stage, the Occam’s razor comes into play, naturally integrated in the Bayesian model choice, which ultimately privileges the model with only one DHO, as it involves fewer free parameters. Imagine, instead, that

Data discussed in Ref. [25] provide another example of the efficacy of Bayesian inference in enforcing the parsimony principle. Specifically, we refer to the case of an IXS measurement from a suspension of gold nanoparticles in water which has been analyzed with a model similar to the one in Eq. (14), yet with the DHO terms replaced by Dirac delta functions, due to the extremely narrow width of the measured excitations. For all

As a further remark, we would like to stress again the fact that results from Bayesian inference are always to be interpreted in a probabilistic nuance. For instance, we stated before that the oscillation mode *credibility interval*, is obtained by sorting the values of

### 4.2 Bayesian inference in the time domain

Time correlation function decays can be modeled in terms of an expansion of the intermediate scattering function

where *j*-th component of the exponential mixture, *i*-th observation and

The value of *j*-th exponential can be either stretched or not with a priori the same probability, for all

Let us consider one of the datasets in Ref. [15], representing the time correlation decay of a polymer solution of polyethylene glycol with a molecular weight of *k* are found in Table 1 .

The most visited model is the one with two exponential functions. The fit is shown in the figure below ( Figure 9 ).

The values reported in Table 1 clearly show that the posterior distribution of

When we model a spectroscopic dataset through a homogeneous mixture, e.g., a linear combination of exponentials, Lorentzians or DHO functions, the posterior distribution for the number of components always has at least a maximum, unless the data are so scarcely informative that the posterior for *insane* and hopeless to confer a distinct physical meaning to each one of the corresponding characteristic relaxation times.

Let us introduce a quantity which could resemble the

which measures the distance between the experimental data and the best fit determined with the *very broad* and slowly decaying, the average of this parameter could be severely affected by the presence of these sizable distribution tails. In these cases, the mode of the distribution should be used instead to estimate the parameter.

k | s ^{2} |
---|---|

Nevertheless,

In summary, we have here shown some of the opportunity offered by a Bayesian inference analysis of experimental results and, in particular, those obtained with spectroscopic methods. As possible future development, it appears very promising the opportunity of applying similar methods to the joint analysis of complementary time or frequency-resolved measurements. Also, we can envisage the use of more informative priors implementing the fulfillment of sum rules of the spectra or any other known physical constraint of the measurement. We are confident that, in the long run, these methods will improve the rigor of routine data analysis protocols, supporting a probability-based, unprejudiced interpretation of the experimental outcome.

This work used resources of the National Synchrotron Light Source II, a US Department of Energy (DOE) Office of Science User Facility operated for the DOE Office of Science by Brookhaven National Laboratory under Contract No. DE-SC 0012704. The open access fee was covered by FILL2030, a European Union project within the European Commission’s Horizon 2020 Research and Innovation programme under grant agreement N°731096.

## Acknowledgments

We would like to thank U. Bafile, E. Guarini, F. Formisano, and M. Maccarini for the very stimulating discussions.