Modeling
AtlasBrowse all 76
Menu
LearnProbabilityMechanisms, distributions, transformations, and limits.Bayesian learningPosterior updates, Thompson sampling, and Bayesian optimization.Deep learningActivations, likelihood-derived objectives, and the architectures built from them.01Activations02Likelihood to loss03ObjectivesSemi-supervisedUse a few labels and many unlabeled examples without hiding the assumptions.ReferenceDistributionsRelationship mapSource & method

Weibull

A lifetime model whose shape controls whether hazard rises, falls, or stays constant.
Type continuousSupport x ≥ 0Family Waiting time
Probability densityf(x) = (k/λ)(x/λ)ᵏ⁻¹e⁻⁽ˣ⁄λ⁾ᵏ
continuous

Weibull in motion

0.00.81.52.33.0
Supportx ≥ 0
Mean0.9027
Variance0.3757
Connections

Where the weibull
comes from — and leads.

Every link is one of the four moves from the relationship map: special cases, transformations, limits, and Bayesian conjugacy.

Exponential← k = 1

Weibull with unit shape has constant hazard and is exponential.

Special case

Zoom out

See every connection at once.

The relationship map draws the whole backbone — special cases, transformations, limits, and conjugacies — in one guided view.

Open the map