measure-theory #

Visualizes measurability (preimages of measurable sets) using a blocky domain X mapped by f into codomain Y, highlights the preimage f^{-1}(B) for a moving measurable set B, shows sigma-algebra closure cues (complement/union), and animates the Lebesgue integral construction from indicators to simple functions to limits and signed functions, with mu(A) displayed as a filling measure bar.

canvasclick to interact

⏮◀◀▶▶STEP0.25x1xZOOM

t=0s

practical uses #

01.Probability theory foundations (events as measurable sets, probabilities as measures)
02.Lebesgue integration for expectations and continuous distributions
03.Stochastic processes and advanced statistics (measurability, filtrations, Lp spaces)

technical notes #

Pure Canvas2D; responsive scaling via scale=min(w,h)/240 and 6px grid snapping for a retro block aesthetic. Uses a 4-step 4.2s cycle with easing to highlight (1) indicators, (2) simple functions, (3) monotone limits, (4) signed decomposition. A lightweight particle burst suggests countable unions; all colors are GREEN/GREEN_DIM on black.

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