Booklet Page Calculator
Calculate saddle-stitch or signature booklet page counts, blank padding pages, duplex sheets, paper weight, and imposition spread order.
Open toolMutual Information Calculator
Calculate mutual information, marginal probabilities and marginal/joint entropies from contingency counts. Provide Contingency counts: one row per line, Logarithm base to compute results directly in your browser. Counts are normalized by a positive total. Zero cells contribute zero; zero marginal rows/columns remain visible. Base > 1 determines information units. No bias correction or significance test is applied. Numeric output uses up to 12 significant digits; floating-point rounding can occur.
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Enter the inputs, then select Calculate.
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How the Mutual Information Calculator Works & Core Formula
The Mutual Information Calculator evaluates Contingency counts: one row per line, Logarithm base in client-side JavaScript using the deterministic rule below and updates the output panel immediately without uploading data to a server.
Calculate mutual information, marginal probabilities and marginal/joint entropies from contingency counts.
Counts are normalized by a positive total. Zero cells contribute zero; zero marginal rows/columns remain visible. Base > 1 determines information units. No bias correction or significance test is applied. Numeric output uses up to 12 significant digits; floating-point rounding can occur.
Mutual Information Calculator Quick Reference & Input Guide
Review the input parameters, allowed value ranges, and worked reference cases for the Mutual Information Calculator below before running your calculation.
| Parameter / Scenario | Default / Sample Value | Rule or Allowed Range | Output / Usage Note |
|---|---|---|---|
| Input: Contingency counts: one row per line | 10,0 0,10 | Nonnegative rectangular counts, up to 30 by 30; finite entries within 1e6. | Required workspace parameter |
| Input: Logarithm base | 2 | Range: 1.000000001 to 1000000.0 | Required workspace parameter |
| Worked Case 1: Baseline run (Contingency counts: one row per line = 10,) | Preloaded / test values | Calculate mutual information, marginal probabilities and marginal/joint entropies from contingency counts | Contingency counts: one row per line = 10,0 0,10; Logarithm base = 2. Output excerpt: Mutual information: 1; Row probabilities: 0.5, 0.5; Column probabilities: 0.5, 0.5; Row entropy: 1. |
| Worked Case 2: Comparison scenario (Normalized Mutual Information Calculator) | Preloaded / test values | Calculate mutual information, marginal probabilities and marginal/joint entropies from contingency counts | Contingency counts: one row per line = 5,5 5,5; Logarithm base = 2. Output excerpt: Mutual information: 0; Row probabilities: 0.5, 0.5; Column probabilities: 0.5, 0.5; Row entropy: 1. |
Step-by-Step Guide to Using the Mutual Information Calculator
Follow these three steps to configure Contingency counts: one row per line, Logarithm base, verify unit and boundary rules, and export your Mutual Information Calculator output.
Enter the inputs
Start with contingency counts: one row per line. Nonnegative rectangular counts, up to 30 by 30; finite entries within 1e6.
Input and settings
Check units and mode
Counts are normalized by a positive total. Zero cells contribute zero; zero marginal rows/columns remain visible. Base > 1 determines information units. No bias correction or significance test is applied. Numeric output uses up to 12 significant digits; floating-point rounding can occur.
Tool result
Calculate and save
Select Calculate, check the labelled result, then use Copy result or Download result.txt.
Review the details
Practical Worked Examples for Mutual Information Calculator
These concrete scenarios illustrate how the Mutual Information Calculator processes baseline inputs, comparison checks, and boundary constraints.
Baseline run (Contingency counts: one row per line = 10,)
Contingency counts: one row per line = 10,0 0,10; Logarithm base = 2. Output excerpt: Mutual information: 1; Row probabilities: 0.5, 0.5; Column probabilities: 0.5, 0.5; Row entropy: 1.
Comparison scenario (Normalized Mutual Information Calculator)
Contingency counts: one row per line = 5,5 5,5; Logarithm base = 2. Output excerpt: Mutual information: 0; Row probabilities: 0.5, 0.5; Column probabilities: 0.5, 0.5; Row entropy: 1.
Boundary & unit check (Mutual Information Calculator)
When solving for 'mutual information calculator', verify that Contingency counts: one row per line, Logarithm base use consistent units and stay within the documented scope: Counts are normalized by a positive total. Zero cells contribute zero; zero marginal rows/columns remain visible. Base > 1 determines information units. No bias correction or significance test is applied. Numeric output uses up to 12 significant digits; floating-point rounding can occur.
How to Read and Verify the Mutual Information Calculator Output
The output panel reports the exact result derived from Contingency counts: one row per line, Logarithm base alongside any unit or formatting notes.
Every calculation follows the rule: Calculate mutual information, marginal probabilities and marginal/joint entropies from contingency counts. Because JavaScript uses IEEE 754 double-precision floating-point arithmetic, numeric outputs display up to 12 significant digits where applicable.
Always cross-check the displayed result against the scope note (Counts are normalized by a positive total. Zero cells contribute zero; zero marginal rows/columns remain visible. Base > 1 determines information units. No bias correction or significance test is applied. Numeric output uses up to 12 significant digits; floating-point rounding can occur) before applying the value to production specifications, financial models, or lab records.
Scope Limits, Accuracy Assumptions & Related Everyday Math Tools
Review these boundary constraints and validation rules before relying on the Mutual Information Calculator output.
Operational scope: Counts are normalized by a positive total. Zero cells contribute zero; zero marginal rows/columns remain visible. Base > 1 determines information units. No bias correction or significance test is applied. Numeric output uses up to 12 significant digits; floating-point rounding can occur. Inputs that violate minimum/maximum bounds or required format rules are rejected with a clear diagnostic prompt.
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Open toolFrequently Asked Questions About Mutual Information Calculator
Answers about using Mutual Information Calculator and understanding its results.
What formula or rule does the Mutual Information Calculator use?
The Mutual Information Calculator applies the rule: Calculate mutual information, marginal probabilities and marginal/joint entropies from contingency counts. Enter Contingency counts: one row per line, Logarithm base into the workspace and the tool computes the exact output in your browser.
What input parameters and units are required for Mutual Information Calculator?
Yes. Provide Contingency counts: one row per line, Logarithm base using the documented units and format shown in the quick reference table above. Preloaded default values let you test the workflow immediately.
What are the accuracy limits and boundary assumptions for Mutual Information Calculator?
Counts are normalized by a positive total. Zero cells contribute zero; zero marginal rows/columns remain visible. Base > 1 determines information units. No bias correction or significance test is applied. Numeric output uses up to 12 significant digits; floating-point rounding can occur. Confirm that your inputs fall within the allowed ranges before exporting the result.
Is the Mutual Information Calculator free and does it keep my inputs private?
Yes. All calculations for the Mutual Information Calculator execute 100% locally in your browser's JavaScript runtime. No inputs or results are uploaded to any external server.
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