How do I simulate a distribution fit?

Open this page and use the live Anomaly Detection tool below — set your inputs and the simulation runs instantly in your browser using real numerics. No install, no account needed.

Yes. The simulation runs free in your browser. A one-time unlock or a Pro plan adds advanced parameters, saved presets, data import, and clean exports.

Can I use my own numbers?

Absolutely — every input is adjustable, and with data import you can drive a distribution fit from your own measurements.

Use case · powered by Anomaly Detection

Anomaly Detection for a distribution fit

Simulate a distribution fit live in your browser. This runs the real Anomaly Detection solver — adjust the inputs, watch it respond instantly, and export the result. No install, no account.

For:Students Researchers Educators Engineers Hobbyists & Makers K-12 Students First Responders

Distributions

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Probability DistributionsLive

PDF / PMF explorer

Controls

Presets

Mean μ

Std dev σ

▶ Run in Python

Data Inspector

Distributionnormal

Mean0.000

Variance1.000

Std dev1.000

Governing equation

f (x) = \frac{1}{2 π 1.00} e^{- \frac{( x - 0 ) ^{2}}{2 \cdot 1.00}}

Reading this result: The mean μ only slides the bell sideways while σ sets its width — variance is σ² = 1.00, so doubling σ quadruples the spread.

Runs locally in your browser — free forever. Scale to the cloud when reality gets heavy.

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Kmeans

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k-Means ClusteringLive

Lloyd's algorithm · live

Controls

Speed1×

Presets

k (centroids)

True clusters

Lloyd's algorithm: assign each point to its nearest centroid, then move each centroid to the mean of its members. Repeat until stable. Try setting k different from the true cluster count.

▶ Run in Python

Data Inspector

Iteration0

Inertia0.00e+0

Governing equation

J = k = 1 \sum 4 x \in C_{k} \sum ∥ x - μ_{k} ∥^{2}, μ_{k} = \frac{1}{∣ C _{k} ∣} x \in C_{k} \sum x

Reading this result: k matches the 4 true clusters, so each centroid can settle onto one real group and inertia falls to a clean minimum.

Runs locally in your browser — free forever. Scale to the cloud when reality gets heavy.

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About this simulation

The full Anomaly Detection tool models a distribution fit with the same numerics engineers and scientists use — running entirely client-side. Change any parameter and the result updates in real time, so you can build intuition, check a design, or teach the concept without spreadsheets or installs.

More you can do with Anomaly Detection

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Other ways to simulate a distribution fit

Optimize + UQ for a distribution fit Hypothesis Test for a distribution fit Linear Regression for a distribution fit Central Limit Theorem for a distribution fit Confidence Intervals for a distribution fit Bayesian Inference for a distribution fit

Frequently asked questions

How do I simulate a distribution fit?: Open this page and use the live Anomaly Detection tool below — set your inputs and the simulation runs instantly in your browser using real numerics. No install, no account needed.
Is it free?: Yes. The simulation runs free in your browser. A one-time unlock or a Pro plan adds advanced parameters, saved presets, data import, and clean exports.
Can I use my own numbers?: Absolutely — every input is adjustable, and with data import you can drive a distribution fit from your own measurements.