The links (by chapter) in this page are for codes, examples, or further derivations and extensions that were referred to in the text.
Chapter  Page  Description 

Chapter 4  p. 73 
Any other criterion that is used for solving problem (4.2) or (4.19) must yield a solution with a lower entropy value—a more informed one where the number of minimal yes/no questions must be lower. (See later chapters and the
For a visual representation of the entropy function, check 
Chapter 4  p. 90 
One to one correspondence between Hessians of the primal and of the dual models: "This corresponde is evident when the numerical solutions are coded. But information theory and relative entropy offer a more intuitive perspective."
Please see the code For more information on the unique relationship between the Covariances of the primal and dual, see, for example, Appendix C of Chapter 3 in Golan, Judge and Miller (1996). 
Chapter 4  p. 93 
The exact formulation of the Hessian and the covariance for all formulations discussed are included in 
Chapter 4  p. 104 
Exercise 16 (Computer Practice II: Maximizing Entropy Versus Maximizing Other Objective Functions): Use any one of the codes referred to in Chapter 4 and use (or add) any criterion (objective function) you like. 
Chapter 4  p. 104 
Exercise 17: different examples and graphical analyses of comparing the maximum entropy method with other informationtheoretic methods that use the same constraints but different entropies as objective functions can be found
in 
Chapter 5  p. 116 
The codes for generating the data and solving the firm size problems are available in 
Chapter 5  p. 131 
A simpler version of the code (for aggregating networks with a small number of nodes) is code is under revision; to be added 
Chapter 5  p. 132 
Exercise 6 (Multidimensional Size Distribution): see 
Chapter 6  p. 140 p. 142 
This code is for transformation of the original data (daily minimum and maximum temperature) into n and k as is shown in Appendix 6A. As an example it presents the transformation of the NYC, March 2013 data. The results are also shown in the
table. The LA example is available in 
Chapter 6  p. 143 
The estimated T and w in this case are 11.11 and 0.29 respectively for 1931, and 142.86 and 5.29 respectively for 2013. The signs of the w’s in this case are also consistent with our earlier argument (Section 2.4) since the integer n is both negative and positive. 
Chapter 6  p. 145 p. 159 
The code for generating these data and that for other related experiments, as well as detailed solution and diagnostics, is available in Appendix 6B. An example of step 1 in Mathematica is below: nobs=100 (* number of individuals *) (* Symptom 1: hyperactivity *) X1=RandomVariate[NormalDistribution[0,2],nobs]; (* Symptom 2: dislexia *) u2=RandomVariate[UniformDistribution[{0,1}],nobs]; X2=Map[If[#>0.8,1,0]&,u2]; (* Symptom 3: attention deficit *) X3=Map[If[#>1,1,0]&,X1]; (* Environmental Impact: single parent household *) u4=RandomVariate[UniformDistribution[{0,1}],nobs]; X4=Map[If[#>0.8,1,0]&,u4] (* Other: age in years *) X5=RandomVariate[UniformDistribution[{3,18}],nobs]; 
Chapter 6  p. 146 
Marginal effects  to be added 
Chapter 6  p. 155 
Though this is not a precise number, the lack of precision does not bias the inferred probabilities (in a significant way) in this case, as long as that value is in the neighborhood of the correct magnitude. (See exercises, and chapter 6 examples in the codes and examples section.) 
Chapter 7  p. 171 (Box 7.1) p. 188 
For the direct relationships among these covariances, or for the transformation between the covariance in parameter space to the probability space, see, for example, appendix 3C in Golan, Judge and Miller (1996).
Please see the code 
Chapter 8  p. 226 
Exercise 3 (Relative Entropy and Maximum Entropy—Computer Practice): Codes for Cross Entropy are available in all commonly used software packages (Stata, SAS
< Matlab, GAMS, Python). Please see the codes and examples section. Codes of interest include 
Chapter 8  p. 227 
Exercise 5 (Grouping: Size Distribution): User needs to modify the code in 
Chapter 11  p. 324 (Box 11.2) 
See the codes related to matrix balancing (

Chapter 12 
See the smoking example ( 

Chapter 12  pp. 345346 
Data related to the example is available in the "Data for Examples" section. Please refer to the binomial (and multinomial) choice examples in the "Codes and Examples" section (specifically, 
Chapter 13  p. 401 
Please refer to the "linear" examples in the "Codes and Examples" section (specifically 
Chapter 14  p. 412 
For option pricing, please refer to 
Chapter 14  p. 423 (Box 11.2) p. 424 
Predicting Coronary Artery—The analysis can be performed in Stata, SAS or GAMS. Users should remember to transform the original data as described in the text. Also refer to 
Chapter 14  p. 434 p. 437 
Please refer to 