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Open toolAIC BIC Calculator
Calculate AIC, BIC and the conditional small-sample correction from a supplied log-likelihood. Provide Log-likelihood, Estimated parameter count k, Sample size n to compute results directly in your browser. AIC = 2k − 2LL; BIC = k ln(n) − 2LL; AICc adds 2k(k+1)/(n−k−1). AICc is N/A when n ≤ k+1. Compare models only on compatible response data, samples and likelihood conventions. Numeric output uses up to 12 significant digits; floating-point rounding can occur.
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How the AIC BIC Calculator Works & Core Formula
The AIC BIC Calculator evaluates Log-likelihood, Estimated parameter count k, Sample size n in client-side JavaScript using the deterministic rule below and updates the output panel immediately without uploading data to a server.
Calculate AIC, BIC and the conditional small-sample correction from a supplied log-likelihood.
AIC = 2k − 2LL; BIC = k ln(n) − 2LL; AICc adds 2k(k+1)/(n−k−1). AICc is N/A when n ≤ k+1. Compare models only on compatible response data, samples and likelihood conventions. Numeric output uses up to 12 significant digits; floating-point rounding can occur.
AIC BIC Calculator Quick Reference & Input Guide
Review the input parameters, allowed value ranges, and worked reference cases for the AIC BIC Calculator below before running your calculation.
| Parameter / Scenario | Default / Sample Value | Rule or Allowed Range | Output / Usage Note |
|---|---|---|---|
| Input: Log-likelihood | -100 | Range: -1000000000.0 to 1000000000.0 | Required workspace parameter |
| Input: Estimated parameter count k | 2 | Range: 0 to 1000 | Required workspace parameter |
| Input: Sample size n | 100 | Range: 1 to 1000000000 | Required workspace parameter |
| Worked Case 1: Baseline run (Log-likelihood = -100) | Preloaded / test values | Calculate AIC, BIC and the conditional small-sample correction from a supplied log-likelihood | Log-likelihood = -100; Estimated parameter count k = 2; Sample size n = 100. Output: AIC: 204; BIC: 209.210340372; AICc: 204.12371134. |
| Worked Case 2: Comparison scenario (Comparison log-likelihood check) | Preloaded / test values | Calculate AIC, BIC and the conditional small-sample correction from a supplied log-likelihood | Log-likelihood = -20; Estimated parameter count k = 3; Sample size n = 4. Output: AIC: 46; BIC: 44.1588830834; AICc: N/A: n must exceed k + 1. |
Step-by-Step Guide to Using the AIC BIC Calculator
Follow these three steps to configure Log-likelihood, Estimated parameter count k, Sample size n, verify unit and boundary rules, and export your AIC BIC Calculator output.
Input and settings
Check units and mode
AIC = 2k − 2LL; BIC = k ln(n) − 2LL; AICc adds 2k(k+1)/(n−k−1). AICc is N/A when n ≤ k+1. Compare models only on compatible response data, samples and likelihood conventions. 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 AIC BIC Calculator
These concrete scenarios illustrate how the AIC BIC Calculator processes baseline inputs, comparison checks, and boundary constraints.
Baseline run (Log-likelihood = -100)
Log-likelihood = -100; Estimated parameter count k = 2; Sample size n = 100. Output: AIC: 204; BIC: 209.210340372; AICc: 204.12371134.
Comparison scenario (Comparison log-likelihood check)
Log-likelihood = -20; Estimated parameter count k = 3; Sample size n = 4. Output: AIC: 46; BIC: 44.1588830834; AICc: N/A: n must exceed k + 1.
Boundary & unit check (Aic Bic Calculator)
When solving for 'aic bic calculator', verify that Log-likelihood, Estimated parameter count k, Sample size n use consistent units and stay within the documented scope: AIC = 2k − 2LL; BIC = k ln(n) − 2LL; AICc adds 2k(k+1)/(n−k−1). AICc is N/A when n ≤ k+1. Compare models only on compatible response data, samples and likelihood conventions. Numeric output uses up to 12 significant digits; floating-point rounding can occur.
How to Read and Verify the AIC BIC Calculator Output
The output panel reports the exact result derived from Log-likelihood, Estimated parameter count k, Sample size n alongside any unit or formatting notes.
Every calculation follows the rule: Calculate AIC, BIC and the conditional small-sample correction from a supplied log-likelihood. 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 (AIC = 2k − 2LL; BIC = k ln(n) − 2LL; AICc adds 2k(k+1)/(n−k−1). AICc is N/A when n ≤ k+1. Compare models only on compatible response data, samples and likelihood conventions. 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 AIC BIC Calculator output.
Operational scope: AIC = 2k − 2LL; BIC = k ln(n) − 2LL; AICc adds 2k(k+1)/(n−k−1). AICc is N/A when n ≤ k+1. Compare models only on compatible response data, samples and likelihood conventions. 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 AIC BIC Calculator
Answers about using AIC BIC Calculator and understanding its results.
What formula or rule does the AIC BIC Calculator use?
The AIC BIC Calculator applies the rule: Calculate AIC, BIC and the conditional small-sample correction from a supplied log-likelihood. Enter Log-likelihood, Estimated parameter count k, Sample size n into the workspace and the tool computes the exact output in your browser.
What input parameters and units are required for AIC BIC Calculator?
Yes. Provide Log-likelihood, Estimated parameter count k, Sample size n 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 AIC BIC Calculator?
AIC = 2k − 2LL; BIC = k ln(n) − 2LL; AICc adds 2k(k+1)/(n−k−1). AICc is N/A when n ≤ k+1. Compare models only on compatible response data, samples and likelihood conventions. 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 AIC BIC Calculator free and does it keep my inputs private?
Yes. All calculations for the AIC BIC Calculator execute 100% locally in your browser's JavaScript runtime. No inputs or results are uploaded to any external server.
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