In the first approach, full details of which can be found in Ref. [3] , various empirical features of the anthropometric data suggested that height and weight together are distributed as a bivariant lognormal random variable with probability density

$\begin{array}{l}{p}_{\left(H,W\right)}\left(h,w\right)\\ =\frac{\exp \left(-\frac{1}{2\left(1-r^2\right)}\left[{\left(\frac{\ln \left(h\right)-m_1}{s_1}\right)}^2-2r\left(\frac{\ln \left(h\right)-m_1}{s_1}\right)\left(\frac{\ln \left(w\right)-m_2}{s_2}\right)+{\left(\frac{\ln \left(w\right)-m_2}{s_2}\right)}^2\right]\right)}{2\pi s_1{s}_{2}hw\sqrt{1-r^2}}\end{array}$ (2)

in which upper case letters (H, W) represent random variables, lower case letters (h, w) represent realizations of the variables (e.g. outcomes of measurement or inputs to calculation), and the associated parameters are defined as follows:

$m_1=\langle \ln \left(H\right)\rangle$ (3)

$s_1^2=\mathrm{var}\left(\ln \left(H\right)\right)=\langle {\left(\ln \left(H\right)-m_1^{}\right)}^2\rangle$ (4)

$m_2=\langle \ln \left(W\right)\rangle$ (5)

$s_2^2=\mathrm{var}\left(\ln \left(W\right)\right)=\langle {\left(\ln \left(W\right)-m_2^{}\right)}^2\rangle$(6)

$r=\mathrm{cov}\left(\ln \left(H\right),\ln \left(W\right)\right)/s_1{s}_2=\langle \left(\frac{\ln \left(H\right)-m_1}{s_1}\right)\left(\frac{\ln \left(W\right)-m_2}{s_2}\right)\rangle$. (7)

Angular brackets $\langle \rangle$ symbolize the expectation value of any discrete function $f_{h,w}$ or continuous function $f\left(h,w\right)$

$\langle f\rangle =\{\begin{array}{l}{\displaystyle \underset{h,w}{\sum }{f}_{h,w}{p}_{h,w}}\\ {\displaystyle \iint f\left(h,w\right)p_{H,W}\left(h,w\right)\text{d}h\text{d}w}\end{array}$ (8)

with summation or integration over a range of non-negative real numbers. The parameter r in Equation (7) is referred to as the linear correlation coefficient; it is a measure of the first-order covariance of two random variables [8] .

The features of the sample distributions of height and weight that suggested a bivariate lognormal distribution included (1) marked asymmetry about the mean as discerned graphically or measured quantitatively by the statistic skewness (related to the 3rd statistical moment), (2) a nonzero correlation coefficient of height and weight, and especially (3) the fact that histograms of the natural logarithms of the sampled height and weight passed statistical tests for normal (i.e. Gaussian) distributions, consistent with the definition of a lognormal distribution [9] .

The statistical content of Equation (2) and its marginal distributions were tested comprehensively against an extensive data base of the Anthropometric Survey of U.S. Army Personnel (ANSUR) [7] . This survey, which included both genders, 5 categories of age ranging from younger than 20 to older than 41, 7 broad categories of race further classified into approximately 30 ethnic subpopulations, 51 US birthplaces (50 States and Washington DC), as well as approximately 30 international birthplaces, is presumed representative of a large, diverse group of basically healthy adults, since members of the military have to pass fitness requirements for acceptance. Statistical tests were carried out separately for male and female cohorts comprising respectively 4082 and 1986 subjects. Figure 1 shows a 3-dimensional plot of the distribution function (2) for the set of parameters that apply to the ANSUR male cohort: $m_1=4.4351$, $m_2=0.5624$, $s_1=0.1654$, $s_2=0.0390$, $r=0.4716$. The second frame of the figure shows an orientation rotated 180˚ relative to the first frame.

For a conjectured distribution with the foregoing characteristics, one might expect the probability density in Equation (2) to approximate a hypothetical “true” probability density reasonably well. In fact, the outcome of the tests revealed agreement between theoretical predictions and data to an astonishing degree.

Upon input of the numerical values of the five parameters of Equations (3)-(7) obtained for each gender from the ANSUR population, the statistical predictions of Equation (2) matched corresponding sample statistics over an extensive hierarchy of higher moments and correlations limited only by statistical uncertainties due to finite sample size. Especially interesting was the fact that the single linear correlation coefficient r, together with the other four parameters, sufficed for correctly predicting all accessible higher-order nonlinear correlations of the data.

The full extent of concordances of lower moments, hyperstatistics, and correlation functions of all four variables $H$, $W$, $\ln \left(H\right)$, $\ln \left(W\right)$, together with exhaustive tests for hidden nonlinear correlations beyond those intrinsic to Equation (2), are discussed in Ref. [3] . It seems improbable that such extensive agreement between data and theory is purely coincidental. Consequently, an explanation of the enigmatic perfection of the bivariate lognormal distribution of human height and weight might be attributable to some fundamental principle. Hence the second approach.

The Principle of Maximum Entropy (PME), introduced by Jaynes in the 1950s as a basis for deriving and justifying the fundamental relations of equilibrium statistical mechanics (ESM) [10] [11] , provides a means of finding the most probable statistical distribution compatible with known prior information. Although the initial motivation was to solve the physics problem of finding a probabilistic, rather than mechanical, explanation of the success of ESM, the PME has since been developed as a general method of inference applicable to problems as diverse as image analysis [12] , detection of cheating [13] , extraction of information from crowdsourcing [14] , and many other applications in science, engineering, and business [15] .

Mathematically, the PME generates a variational procedure by which to maximize the Shannon information entropy function [16] augmented by constraints usually posed in the form of expectation values. The virtue of the PME is that it leads to the most objective (i.e. least biased) probability distribution consistent with the given constraints [17] . The information entropy, which is equivalent to the entropy function of ESM up to a universal scalar factor (the Boltzmann constant), is defined by an expression of the form

$S\left(p\right)\equiv S\left(p_1\cdots p_n\right)=-{\displaystyle \underset{i=1}{\overset{n}{\sum }}{p}_i\ln \left(p_i\right)}$ (9)

in which $p_i\left(i=1,\cdots ,n\right)$ is the probability of an outcome $x_i$. Equation (9) can be generalized to apply to the entropy of a continuous variable, an extension that will be discussed later in the paper where the distinction becomes relevant. From Equations (8) and (9) it follows that the information entropy may be thought of as the expectation

$S\left(p\right)\equiv -\langle \ln \left(p\right)\rangle$. (10)

In Section 2, the maximum entropy distribution of human height and weight is derived. The statistical implications of maximum entropy are taken up in Section 3. An important matter regarding the information entropy of continuous variables is discussed in Section 4. Section 5 outlines a hypothetical stochastic mechanism of biological development that leads to the bivariant lognormal distribution. Some fine points regarding the maximum entropy explanation of the distribution of human height and weight and future steps for further testing are discussed in Section 6.

The foregoing section employing the PME has established that the bivariant lognormal distribution of human height and weight, previously deduced on the basis of empirical sampling, is derivable by a theoretical procedure. The only empirical input in this procedure is the prior information serving as constraints. Apart from this information, the resulting probability density (2) incorporates no additional assumptions, explicit or implicit, introduced through any hypothetical model. In this regard, the PME is said to generate the most objective solution.

To address the question of whether the PME can actually account for the extraordinary match between the theoretical and sampling distributions of human height and weight, one must understand the probabilistic implications of the PME. For illustrative purposes, consider the male cohort (sample size N > 4000) of the Anthropometric Survey of U.S. Army Personnel (ANSUR) [7] , which was the sampling distribution used in Reference [3] to test the statistical predictions of the hypothesized lognormal distribution of height and weight. For simplicity, divide the x-y plane (with x and y the variables in Equation (29)) into a rectilinear grid of $n^2$ cells of equal size labeled by indices ( $i=1,\cdots ,n$; $j=1,\cdots ,n$). Each cell represents a specified range of heights and weights into which an observed number $n_{i,j}$ of sampled individuals fall. Conservation of participants requires that

${\displaystyle \underset{i,j=1}{\overset{n}{\sum }}{n}_{i,j}}=N$. (31)

In what follows it will be more convenient to relabel cells sequentially by a single index, rather than by a Cartesian double index. This can be accomplished by labeling a Cartesian cell $\left(x_i,y_j\right)$ by the index $k=\left(j-1\right)n+i$ in which $k=1,2,\cdots ,n^2$. For examples, cell $\left(x_1,y_1\right)$ becomes cell $k=1$, cell $\left(x_1,y_n\right)$ becomes cell $k=n^2-n+1$, cell $\left(x_n,y_1\right)$ becomes cell $k=n$, and cell $\left(x_n,y_n\right)$ becomes cell $k=n^2$. Then $n_k$ is the number of individuals falling into the $k^{th}$ cell, and Equation (31) becomes

${\displaystyle \underset{k=1}{\overset{K}{\sum }}{n}_k}=N$ (32)

with $K=n^2$ the maximum number of cells. The number of ways $\Omega$ of distributing the possible values $\left\{n_k\right\}$ over the K cells is described by a multinomial distribution

$\Omega =\frac{N!}{n_1!n_2!\cdots n_K!}$ . (33)

In physics, $\Omega$ is referred to as the volume of phase space. Of the vast number of possible ways to partition $N\gg 1$ values over K cells, the partition most likely to occur is the one that maximizes $\Omega$. Applying to Equation (33) the simplest form of Stirling’s approximation [21] of $z!$ for some integer z

$\ln \left(z!\right)\approx z\ln \left(z\right)-z$,(34)

one can show that

$\frac{1}{N}\ln \left(\Omega \right)\approx -{\displaystyle \underset{k=1}{\overset{K}{\sum }}\frac{n_k}{N}\ln \left(\frac{n_k}{N}\right)}$.(35)

In the limit of large (technically infinite) N, the ratio ( $n_k/N$) approaches the probability $p_k$, whereupon one has to an excellent approximation the relation

$\frac{1}{N}\ln \left(\Omega \right)\stackrel{N\gg 1}{\to }S\left(p\right)$ (36)

between sampling frequency and entropy. Therefore, finding the distribution of frequencies $\left\{n_k\right\}$ that maximizes $\Omega$, Equation (33), is equivalent to finding the distribution of probabilities $\left\{p_{x,y}\right\}$ that maximizes the information entropy S, Equation (12). One can therefore rearrange Equation (36) to write

$\Omega ={\text{e}}^{NS}$. (37)

Suppose next that ${\Omega }_{\text{ME}}$ is the phase space volume of frequencies $\left\{n_k\right\}$ with maximum entropy $S_{\max }$, and $\Omega$ is the phase space volume of some other distribution having the same constraint Equation (32) with lower entropy S. Then from Equation (37) the ratio ${\Omega }_{\text{ME}}/\Omega$ in terms of the entropy difference $\Delta S$ is given by

$\frac{{\Omega }_{\text{ME}}}{\Omega }=\exp \left(N\left(S_{\text{max}}-S\right)\right)=\exp \left(N\Delta S\right)$. (38)

The implications of Equation (38) can be astounding. Consider a sample size comparable to that of the ANSUR male cohort approximated to be 4000, and two distributions that differ in entropy by only 0.01. Then evaluation of (38) leads to $\left({\Omega }_{\text{ME}}/\Omega \right)>{10}^{17}$. In words, the maximum entropy distribution ${\Omega }_{\text{ME}}$ is one hundred thousand million million times more likely to occur by chance than the distribution $\Omega$.

The foregoing estimate is a prediction. If the height and weight of individuals in some group of people are measured, and the only prior information that an analyst has are the means, variances, and correlation coefficient of the logarithms of the two variables, then the most likely distribution to account for the sample is the bivariate lognormal distribution, such as was initially found empirically [3] . The larger the sample size, the more probable is the maximum entropy distribution to be observed in any sampling. As just illustrated, a maximum entropy distribution can be overwhelmingly more probable than any other distribution subject to the same constraints. In such cases, how a probability density with just a few parameters can suffice to predict correctly an extensive array of testable moments and correlations is no longer a mystery.

In Section 2 it was shown that a bivariate lognormal PDF, Equation (2), is the maximum entropy solution to the variational equation for the distribution of human height and weight. The procedure entailed first showing that a logarithmic transformation of the variables led to a bivariate normal PDF, Equation (29). It was then argued that a deterministic (in contrast to stochastic) coordinate transformation and its inverse do not change the uncertainty in knowledge about the state of a system. From this line of reasoning, it followed that the inverse transformation of the PDF back to the original variables likewise described a maximum entropy distribution.

The preceding reasoning is not wrong, but there is a subtle underlying issue that should not be glossed over. Since the two distributions, bivariate normal (BN) and bivariate lognormal (BLN), are both maximum entropy distributions of the same system, although expressed in different coordinates, one might have expected that they would have the same value of maximum entropy. There is no fundamental requirement that this be the case, and, in fact, it is not the case. Straightforward calculations using PDFs (29) and (2) lead to the entropy expressions:

$\begin{array}{l}\text{Bivariant Normal}{S}_{\text{BN}}=\ln \left(2\pi e{s}_1{s}_2\sqrt{1-r^2}\right)\\ \text{Bivariant Lognormal}{S}_{\text{BLN}}=\ln \left(2\pi e{s}_1{s}_2\sqrt{1-r^2}{\text{e}}^{m_1+m_2}\right)\end{array}$ (39)

where it is seen that the BLN entropy depends on all five parameters in contrast to the BN entropy which is independent of the two means.

One can understand the reason for this curious discrepancy—although the expressions in Equation (39) are correct—by examining the general relation between two entropy expressions connected by a coordinate transformation. For illustrative purposes consider a coordinate transformation (which can be multidimensional) $x\to y\left(x\right)$ leading to the two entropy expressions $S_x$, $S_y$ defined by

$\begin{array}{l}{S}_x=-{\displaystyle \int p_x\left(x\right)\ln \left(p_x\left(x\right)\right)\text{d}x}\\ S_y=-{\displaystyle \int p_y\left(y\right)\ln \left(p_y\left(y\right)\right)\text{d}y}\end{array}$ (40)

where the probability densities satisfy

$p_x\left(x\right)\text{d}x=p_y\left(y\right)\text{d}y$(41)

and are therefore connected by the transformation

$p_y\left(y\left(x\right)\right)=p_x\left(x\right)\left|\frac{\partial x}{\partial y}\right|=J{p}_x\left(x\right)$ (42)

with Jacobian $J$. Substitution of Equation (42) into Equation (40) leads to the relation

$S_y=S_x-{\displaystyle \int p_x\left(x\right)\ln \left(J\left(x\right)\right)\text{d}x}$ (43)

or more succinctly,

$S_y=S_x-\langle \ln \left(J\right)\rangle$. (44)

Applied to a bivariate coordinate transformation $\left(x_1,x_2\right)\to \left(y_1,y_2\right)$ as a concrete example, Equations (40), (42), and (43) reduce to the explicit form

$S_y=-{\displaystyle \int p_x\left(x_1,x_2\right)\ln \left(\frac{p_x\left(x_1,x_2\right)\text{d}{x}_1\text{d}{x}_2}{\text{d}{y}_1\text{d}{y}_2}\right)\text{d}{x}_1\text{d}{x}_2}$ (45)

or

$S_y-S_x=-{\displaystyle \int p_x\left(x_1,x_2\right)\ln \left(\frac{\text{d}{x}_1\text{d}{x}_2}{\text{d}{y}_1\text{d}{y}_2}\right)\text{d}{x}_1\text{d}{x}_2}$. (46)

Apart from a minus sign, the right side of Equation (46) takes the form of a Kullback-Leibler (K-L) divergence [22] [23] , which quantifies the difference between two probability distributions. It is thus seen that the difference in entropies in Equation (46) is attributable to different prior distributions of the two sets of volume elements. $S_x$ is calculated on the presumption that each volume element $\text{d}x\equiv \text{d}{x}_1\text{d}{x}_2\cdots \text{d}{x}_n$ (of an n-dimensional system) is equally likely, whereas $S_y$ is calculated presuming each volume element $\text{d}y\equiv \text{d}{y}_1\text{d}{y}_2\cdots \text{d}{y}_n$ is equally likely. However, since $y$ is a function of $x$, the volume elements of the two systems of coordinates cannot in general both be uniformly distributed. Hence the coordinate transformation $x\to y\left(x\right)$ leads to a difference in entropies.

The fact that entropy $S_y$ in Equation (43) or (44) differs in value from entropy $S_x$ does not mean that variations of the corresponding entropy functionals $H_x$, $H_y$—from which the maximum entropy PDFs are obtained—are necessarily different. Consider the logarithmic transformation $\left(X=\ln \left(H\right),Y=\ln \left(W\right)\right)$ of the variables $\left(H,W\right)$and the inverse transformation $\left(H={\text{e}}^X,W={\text{e}}^Y\right)$. The Jacobian of the transformation is

$J=\left|\frac{\partial \left(X,Y\right)}{\partial \left(H,W\right)}\right|=\frac{1}{HW}=\exp \left(-\left(X+Y\right)\right)$. (47)

Substitution of Equation (47) into Equation (44) leads to the relation

$S_{H,W}=S_{X,Y}-\langle \ln \left(J\right)\rangle =S_{X,Y}-\langle X\rangle -\langle Y\rangle$. (48)

Recall that the two expectation values on the right side of Equation (48) are respectively $m_1$ and $m_2$, which are part of the prior information, Equations (3) and (5). Given the entropy functional $H_{X,Y}$ of Equation (13), the functional $H_{H,W}$ associated with entropy $S_{H,W}$ of Equation (48) would then include two additional constraints and take the form

$H_{H,W}=H_{X,Y}\left(p_{x,y}\right)-{\lambda }_4{\displaystyle \underset{x,y}{\sum }x{p}_{x,y}}-{\lambda }_5{\displaystyle \underset{x,y}{\sum }y{p}_{x,y}}$ (49)

where $H_{X,Y}\left(p_{x,y}\right)$ includes the terms with Lagrange multipliers ${\lambda }_j$ $\left(j=0,1,2,3\right)$. However, information on the mean values of X and Y is already included in the constraints on the variances of X and Y in $H_{X,Y}\left(p_{x,y}\right)$. Thus the two additional terms in Equation (49) constitute redundant information, whereupon the Lagrange multipliers ${\lambda }_4$ and ${\lambda }_5$ would vanish. It is then apparent that the variation $\delta H_{H,W}=0$ since $\delta H_{X,Y}=0$, and, as expected, the bivariant lognormal PDF is the maximum entropy solution in coordinates $\left(h,w\right)$ given that the bivariant normal PDF is the maximum entropy solution in coordinates $\left(x=\ln \left(h\right),y=\ln \left(w\right)\right)$.

As a general procedure consistent with Bayesian principles, Jaynes [17] proposed a relative entropy of the form

$S_{\text{rel}}=\{\begin{array}{l}-{\displaystyle \int p\left(x\right)\ln \left(p\left(x\right)/m\left(x\right)\right)\text{d}x}\left(\text{continuous}\right)\\ -{\displaystyle \underset{x}{\sum }{p}_x\ln \left(p_x/m_x\right)}\left(\text{discrete}\right)\end{array}$ (50)

for either continuous or discrete random variables. This again is the form of a K-L divergence. The background distribution $m\left(x\right)$ is a measure that transforms under a change of coordinates in the same way as the probability function $p\left(x\right)$, whereupon expression (50) is invariant under a coordinate change.

As employed in applications of the PME, the function $m\left(x\right)$ can be interpreted as a Bayesian prior, i.e. the distribution representing a state of complete ignorance before any empirical information has been acquired [24] . How one determines the appropriate measure for an arbitrary statistical problem is, in general, still a work in progress. In some cases, especially for a finite system of discrete random variables (see [13] for a worked example), the correct measure may be obvious. In more complex cases involving continuous random variables, Jaynes proposed the use of group theoretical methods to deduce $m\left(x\right)$ from the appropriate transformation group that describes the invariances of a statistical problem [24] .

It might be thought that the obvious choice of $m\left(x\right)$ would be a uniform distribution over the full range of possible outcomes x, since that certainly depicts a state of total ignorance. However, a uniform distribution is not always suitable or even possible, especially for a system of continuous random variables. First, a uniform distribution is not normalizable if the range of the variables is infinite. And second, being independent of coordinates, a uniform distribution does not change under a coordinate transformation, so that the expression (50) would again not be invariant.

It is rarely the case, however, that an analyst is in a state of total ignorance prior to data collection. For example, in the problem of the distribution of human height and weight, one can be reasonably assured that no adult human ever had a height exceeding 10 m or a weight exceeding 1000 kg. Thus, a suitable approximation to a uniform distribution could be a Gaussian measure $m\left(x\right)$ with uncertainty sufficiently broad that the center is irrelevant and the function varies very slowly over the practical range of the variables.

Demonstration that an analysis based on the Principle of Maximum Entropy leads naturally to a bivariant lognormal PDF of height and weight does not mean there can be no stochastic physical mechanism that can also generate this statistical distribution. If future anthropometric surveys of larger cohorts are implemented and continue to sustain the bivariant lognormal as the effectively exact distribution of height and weight, there may well be an underlying physical reason. Here is one possible mechanism by which such a distribution can develop.

Suppose, for example, a study of the genetic basis of human height were to reveal that the variable H could be represented by the geometric mean

$H={\left({\displaystyle \underset{i=1}{\overset{n}{\prod }}{X}_i}\right)}^{\frac{1}{n}}$ (51)

of a product of independent random variables $\left\{X_i\right\}$ $\left(i=1,\cdots ,n\right)$ of finite non-vanishing means and variances. Then the logarithm of H would take the form of a sum of independent variables $\left\{\ln \left(X_i\right)\right\}$

$\ln \left(H\right)=\frac{1}{n}{\displaystyle \underset{i=1}{\overset{n}{\sum }}\ln \left(X_i\right)}\to N\left(M_H,{\Sigma }_H^2\right)$, (52)

which, under conditions for which the Central Limit Theorem [25] is applicable, can be approximated by a normal distribution of resulting mean and variance

$M_H=\frac{1}{n}{\displaystyle \underset{i=1}{\overset{n}{\sum }}\langle \ln \left(X_i\right)\rangle },{\Sigma }_H^2=\mathrm{var}\left(M_H\right)$ (53)

as indicated by the arrow in Equation (52). From Equations (52) and (53) it would then follow that the marginal distribution of H is lognormal in form, $H\to \Lambda \left(M_H,{\Sigma }_H^2\right)$. (Upper-case lambda is symbolic of a lognormal distribution.)

Suppose further that the variable W is likewise found to be representable as the geometric mean of independent variables $\left\{Y_i\right\}$ $\left(i=1,\cdots ,n\right)$ with each factor $Y_i$ correlated with the corresponding variable $X_i$. (For example, suppose that the genes associated with each pair of variables $\left(X_i,Y_i\right)$ are located on the same chromosomes.) Then, by the previous argument, the marginal distribution of W would also be lognormal in form, and the full statistics of the two correlated variables $\left(H,W\right)$ would be described by a bivariant lognormal distribution, as is currently found to be the case.

The foregoing mechanism, entailing both a physical cause (biological growth as a product of independent random factors) and a probabilistic reduction (the Central Limit Theorem) is at present entirely hypothetical. As of this writing, the author is not aware of any published studies examining the influence of genetics and/or environment that could account for the statistical distribution of adult human height and weight.

The objective of this paper has been to account by means of the Principle of Maximum Entropy (PME) for the extraordinary predictive capacity of the bivariant lognormal probability density function (PDF) of human height and weight [3] . Currently, there is no known physical reason rooted in the biology of human development for the exactness of this distribution. However, the maximum entropy distribution, derived in Section 2 and elaborated in Section 3, is the most probable distribution consistent with constraints imposed by prior information. In the case of human height and weight, the prior information comprised the means, variances, and linear correlation of the logarithms of height and weight.

The PME constitutes a general inferential procedure derived from probability theory and not associated with any specific physical agency. Loosely summarized in words, the PME says that the distribution that maximizes the entropy, subject only to the known information provided at the outset, is the distribution that is most likely to occur in any sampling of the chosen variables. As illustrated in Section 3, the probability of this occurrence can be astronomically greater than the occurrence of any other distribution likewise subject to the same prior information. Thus, the maximum entropy distribution can appear to be an effectively exact distribution for no discernible physical reason.

A question might arise as to whether one can improve the predictive capacity of a maximum entropy distribution by supplying a more inclusive set of prior information. Actually, the PME itself informs a user whether more or different information is needed or not. If the resulting PME solution satisfactorily accounts for a given set of data, then providing more prior information is not likely to yield a statistically significant improvement. However, more or different information is needed if the PME solution does not adequately account for the data. Moreover, if the PME variational procedure leads to no solution, then the prior information is either mutually inconsistent, or else does not reflect the conditions of the experiment or observations that generated the data. Further scrutiny of the physical nature of the problem in question could then suggest to the user what revisions to make.

As illustration of the preceding comments, consider first the case of equilibrium statistical mechanics (ESM) initially derived by J. Willard Gibbs in the 19th Century and given a modern PME justification by E. T. Jaynes [10] . Application of the PME with prior information comprising just the mean values of the internal energy and the number of particles in the system leads to a probability density function of exponential form. From this PDF one can predict correctly a wide range of macroscopic equilibrium properties of both classical and quantum systems, including the fluctuations (i.e. predictive uncertainties) of the variables. Since ESM is ordinarily applied to systems with an enormous number N of degrees of freedom (e.g. $N~{10}^{24}$ for 1 mol of a monatomic gas), no further prior information beyond the expectation of energy and particle number is required, since the variances of the means decrease as $1/N$.

However, for the problem of the distribution of human height (H) and weight (W), prior information consisting of just the mean values $\left(\langle H\rangle ,\langle W\rangle \right)$ will not suffice. This would lead to an exponential PDF for $\left(H,W\right)$, which does not match the data. Including in the prior information the variances $\left(\mathrm{var}\left(H\right),\mathrm{var}\left(W\right)\right)$ will also not suffice. This would lead to a normal PDF for $\left(H,W\right)$, whose sample distribution is not symmetric about the means. Only when the prior information comprises the means and variances and linear correlation of $\left(\ln \left(H\right),\ln \left(W\right)\right)$ does the resulting bivariant lognormal probability density permit accurate prediction of all testable statistics extractable from the ANSUR data.

It may at first thought seem strange that so small a set of prior information makes it possible to predict correctly the asymmetries of the $\left(H,W\right)$ sample distribution without explicit inclusion in the entropy functional of additional terms proportional to the third moments (skewness) of any of the variables. To have included such terms would have greatly complicated the analysis, since the resulting distributions would no longer be lognormal in form or even be reducible to a closed form expression. However, the extra work would have achieved little if anything, since contributions of skewness to the entropy functional decrease as $1/N^2$ for a sample size N. Recall that $N>4000$ for the ANSUR male cohort. Thus, for large sample size N the PME analysis of height and weight no more needed prior information on skewness than did the PME analysis of equilibrium statistical mechanics need prior information on variance.

Consequently, at this present stage in the statistical analysis of human height and weight, there would be no compelling reason to modify the prior information without first having more extensive data. Because the sampling variance of a moment depends on the population moment of twice the order [26] , the uncertainties of higher-order statistical moments (hyperstatistics) increase rapidly with order, and tests of the maximum entropy PDF to predict such statistics will require correspondingly larger sample sizes than the large ANSUR data base used in References [2] and [3] .

From the foregoing comments on prior information, sample size, and PME solutions, one can draw an important epistemological lesson. Expressed loosely, the probability density function does not describe attributes of the real world; rather, it describes one’s state of knowledge about the real world. Thus, the idea that there exists a unique “true” probability density function of human height and weight which may emerge from surveys of ever larger populations is illusory. What can emerge, of course, is a more refined PDF that attains greater predictive capability. Nevertheless, it is conceivable that more than one PDF, each a PME solution for a different set of prior information, can account for the same set of data with comparable success. This does not appear to be the case for human height and weight, although the author has encountered such a situation in his analysis of the probability of infectivity of SARS-Cov 2 (COVID) [27] .

Dr. M. P. Silverman is the G. A. Jarvis Professor of Physics Emeritus at Trinity College and senior scientist at Tall Pines Research. His areas of research are in nuclear and medical physics. This article was conceived and written by him alone with no reliance on artificial intelligence.

The author is not affiliated with any commercial companies or organizations and has received no compensation for the composition of this article.

The author expresses his gratitude to Dr. S. B. Brachwitz for numerous discussions on matters relating to biological development. He would also like to thank the reviewers for their helpful comments.