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common-distributions #

Visualizes the difference between discrete vs continuous support by switching between PMF bars and PDF curves, then shows normalization (sum/integral → 1) and how parameters θ determine moments (mean/variance) for Bernoulli, Binomial, Poisson, Uniform, and Normal families.

canvasclick to interact

⏮◀◀▶▶STEP0.25x1xZOOM

t=0s

practical uses #

• 01.Choosing an appropriate model for counts vs measurements (PMF vs PDF)
• 02.Checking whether a hand-built probability function is valid via normalization
• 03.Understanding how parameters (p, λ, μ, σ, a, b) control mean and variance for simulation and inference

technical notes #

Single self-contained Canvas2D draw function. Cycles through 5 distributions in ~4.2s segments, animates a point-probability (discrete) or interval area (continuous), approximates normalization by summation or midpoint-rule integration, and uses grid-snapped rectangles/lines for a retro blocky green-on-black aesthetic.

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