Mathematics · Probability
Total Correlation Multi-Information Calculator
Calculate total correlation from sum of marginal entropies and joint entropy.
Inputs and results stay in this browser. Change one value at a time to explore the relationship.
Calculation steps
- Use c=a−b with sum of marginal entropies=7.2 and joint entropy=5.8.
- total correlation=1.4000000000000004.
Understand Total Correlation Multi-Information
One idea, three depths
Choose how deeply to explain Total Correlation Multi-Information
Total Correlation Multi-Information: Calculate total correlation from sum of marginal entropies and joint entropy.
Age 5Explain it to a 5-year-oldStart with a picture
Imagine using Total Correlation Multi-Information to answer this question: calculate total correlation from sum of marginal entropies and joint entropy? Enter sum of marginal entropies and joint entropy; the calculator shows total correlation. For example: sum of marginal entropies=7.2 and joint entropy=5.8 produce total correlation=1.4000000000000004. The answer tells you total correlation.
Age 15Explain it to a 15-year-oldConnect it to the formula
Total correlation subtracts joint entropy from the sum of all marginal entropies to measure multivariate dependence. This page evaluates the relationship directly. The rule is c=a−b. Its input values are sum of marginal entropies, joint entropy, and the main result is total correlation. For example: sum of marginal entropies=7.2 and joint entropy=5.8 produce total correlation=1.4000000000000004.
CollegeExplain it at college levelState the model precisely
This calculator evaluates the stated total correlation multi-information relation over the valid real-number domain stated below. The implemented relation is c=a−b, evaluated from sum of marginal entropies, joint entropy to produce total correlation. Total correlation subtracts joint entropy from the sum of all marginal entropies to measure multivariate dependence. This page evaluates the relationship directly. All marginals and the joint distribution must use the same variables, probability model, and logarithm base.
Inputs and valid domain
- sum of marginal entropies must be a finite real number.
- joint entropy must be a finite real number.
Important boundary: All marginals and the joint distribution must use the same variables, probability model, and logarithm base.
The formula
c=a−b
How the calculator works through it
It substitutes sum of marginal entropies, joint entropy into the formula and exposes every numerical step above. The main output is total correlation.
Read the result correctly
The total correlation is the direct answer to “calculate total correlation from sum of marginal entropies and joint entropy.” Read it with the units shown beside the inputs; a sign, angle, percentage or rate changes what the number means.
A worked check
sum of marginal entropies=7.2 and joint entropy=5.8 produce total correlation=1.4000000000000004.
Where this model stops being reliable
All marginals and the joint distribution must use the same variables, probability model, and logarithm base.
Learn it by changing one value
Begin with the worked example, then change one value while keeping the others fixed. Compare the new result and calculation steps to identify which part of the formula changed.
Dictionary terms behind this calculator
Before studying the codeWhat you should know firstUse the calculator immediately, or check the foundations before reading the implementation.
These foundations help you understand why Total Correlation Multi-Information works. They never block the calculator, and “optional” means useful context rather than a hidden requirement.
Hard requirements
- Reading formulas and substituting values
Total Correlation Multi-Information uses c=a−b. You need to recognise what each side represents before substituting the stated inputs or rearranging the relationship.
Review this foundation about 4 min
Strong support
- Probability as a modelled proportion
Probability rules are needed to interpret what the Total Correlation Multi-Information result says about possible outcomes.
Review this foundation about 5 min
Optional enrichment
- Ordered arrangements
Counting ordered arrangements can extend Total Correlation Multi-Information to more detailed sample spaces and event models.
Review this foundation about 5 min
Mathematics → algorithm → program
Implement this calculation in code
These are direct reference implementations of the calculator's principal relationship and first output. They run locally and include a small known-answer check where the language supports it.
Algorithm
- Read sum of marginal entropies, joint entropy.
- Evaluate the principal relationship: c=a−b.
- Return total correlation and check the domain conditions described above.
Python
from math import *
def total_correlation_multi_information_calculator(a, b) -> float:
return (a - b)
assert abs(total_correlation_multi_information_calculator(7.2, 5.8) - 1.4000000000000004) < 1e-6 * max(1.0, abs(1.4000000000000004))
C
#include <assert.h>
#include <math.h>
double total_correlation_multi_information_calculator(double a, double b) {
return (a - b);
}
int main(void) {
const double expected = 1.4000000000000004;
const double actual = total_correlation_multi_information_calculator(7.2, 5.8);
assert(fabs(actual - expected) < 1e-6 * fmax(1.0, fabs(expected)));
}
C++
#include <cassert>
#include <cmath>
#include <numbers>
double total_correlation_multi_information_calculator(double a, double b) {
return (a - b);
}
int main() {
constexpr double expected = 1.4000000000000004;
const double actual = total_correlation_multi_information_calculator(7.2, 5.8);
assert(std::fabs(actual - expected) < 1e-6 * std::fmax(1.0, std::fabs(expected)));
}
Linux x86-64 assembly
x86-64 NASM · System V ABI · Linux · SSE2 with libm where required
; double total_correlation_multi_information_calculator(double a, double b)
; Linux x86-64 NASM · System V ABI · first eight doubles in xmm0–xmm7
global total_correlation_multi_information_calculator
section .text
total_correlation_multi_information_calculator:
push rbp
mov rbp, rsp
sub rsp, 32
movsd [rbp-8], xmm0
movsd [rbp-16], xmm1
movsd xmm0, [rbp-8]
subsd xmm0, [rbp-16]
movsd [rbp-24], xmm0
movsd xmm0, [rbp-24]
leave
ret
MATLAB
function result = total_correlation_multi_information_calculator(a, b)
result = (a - b);
end
Wolfram Language
ClearAll[mwCalculate];
mwCalculate[a_, b_] := (a - b);
Continue in mathematical software
The downloaded file includes your current inputs and first calculated result. It is created locally.
Floating-point answers can differ slightly by language, compiler and processor. Compare within a suitable tolerance rather than assuming every decimal representation will be identical.
Supporting sourcesAcademic referencesPrimary standards, textbooks and complete citations
Standards, reading and academic references
Use the calculator as the worked interaction, then consult the primary standards and academic textbooks listed below. MW SysArc links to the original sources; the explanation on this page is original and does not reproduce them.
Introductory Statistics 2e
Read the free OpenStax statistics textbookCite this book
- APA 7
- Illowsky, B., & Dean, S. (2023). Introductory statistics 2e. OpenStax. https://openstax.org/books/introductory-statistics-2e/pages/1-introduction
- MLA 9
- Illowsky, Barbara, and Susan Dean. Introductory Statistics 2e. OpenStax, 2023, https://openstax.org/books/introductory-statistics-2e/pages/1-introduction.
- Chicago author-date
- Illowsky, Barbara, and Susan Dean. 2023. Introductory Statistics 2e. Houston, TX: OpenStax. https://openstax.org/books/introductory-statistics-2e/pages/1-introduction.
OpenStax entries are free to read online. Follow the licence shown on each linked source before redistributing or adapting its content.
Reuse the page responsiblyCite this pageAPA, MLA, Chicago, Harvard, BibTeX and RIS
These formats cite this calculator page itself. They are separate from the academic references above, which support the mathematical method and terminology.
APA 7
MW SysArc. (2026, July 21). Total Correlation Multi-Information Calculator. MW SysArc Tools. https://math.mwsysarc.com/probability/total-correlation-multi-information-calculator
MLA 9
MW SysArc. “Total Correlation Multi-Information Calculator.” MW SysArc Tools, 21 July 2026, https://math.mwsysarc.com/probability/total-correlation-multi-information-calculator. Accessed 31 Aug. 2026.
Chicago 17
MW SysArc. “Total Correlation Multi-Information Calculator.” MW SysArc Tools. Published July 21, 2026. Accessed August 31, 2026. https://math.mwsysarc.com/probability/total-correlation-multi-information-calculator.
Harvard
MW SysArc (2026) ‘Total Correlation Multi-Information Calculator’, MW SysArc Tools. Published 21 July 2026. Available at: https://math.mwsysarc.com/probability/total-correlation-multi-information-calculator (Accessed: 31 August 2026).
BibTeX and RIS records
BibTeX
@misc{mwsysarc_total_correlation_multi_information_calculator_2026,
author = {{MW SysArc}},
title = {Total Correlation Multi-Information Calculator},
howpublished = {MW SysArc Tools},
year = {2026},
url = {https://math.mwsysarc.com/probability/total-correlation-multi-information-calculator},
note = {Published July 21, 2026; accessed August 31, 2026}
}RIS
TY - ELEC
AU - MW SysArc
TI - Total Correlation Multi-Information Calculator
T2 - MW SysArc Tools
PY - 2026
DA - 2026-07-21
Y2 - 2026-08-31
UR - https://math.mwsysarc.com/probability/total-correlation-multi-information-calculator
N1 - Published July 21, 2026
ER -Clear answers
Frequently asked questions
What does the Total Correlation Multi-Information do?
Calculate total correlation from sum of marginal entropies and joint entropy.
How does the Total Correlation Multi-Information work?
The calculator applies c=a−b. Total correlation subtracts joint entropy from the sum of all marginal entropies to measure multivariate dependence. This page evaluates the relationship directly.
What can I learn from the Total Correlation Multi-Information?
It connects the mathematical rule to your chosen numbers and shows each calculation step. Change one input at a time to see how the result responds.
Does MW SysArc receive or store what I enter?
No. The calculation runs locally in your browser. MW SysArc does not receive or store your calculation inputs.
How should I use the result?
Use the steps to understand the method, then verify important school or professional work using the notation and rounding rules required in your setting.
Last reviewed . Calculations tested .