Discrete distribution
A discrete probability distribution assigns probability mass to outcomes in a finite or countably infinite set of distinct values.
Fabulous Adventures in Data Structures and Algorithms
A discrete probability distribution assigns probabilities to outcomes in a finite or countably infinite set.
- How it is used
- Replaces direct Random.Next calls with distributions that expose sampling, support, and relative likelihoods.
- How deep it goes
- Defines PMFs, supports, and integer weights, then compares sampling algorithms without developing their full implementations here.
- Examples
- Uses dice, unfair coins, weighted marbles, C# sampling, and filtering categories from categorical distributions.
- What you can do after
- Build finite random-value abstractions with IDiscreteDistribution<T> instead of scattering direct Random calls through program logic.
Read how Fabulous Adventures in Data Structures and Algorithms explains discrete distribution →
Grokking Statistics
A probability distribution whose possible outcomes form a finite or countably infinite set of distinct values.
- How it is used
- Models probabilities for finite categorical, binary, or integer outcomes such as product colors or trial successes.
- How deep it goes
- Defines probability assignment, validates totals, visualizes categories, and samples outcomes, without deriving distribution formulas.
- Examples
- Uses controller color sales, probability tables, bar charts, printed probabilities, and random category sampling.
- What you can do after
- Apply it via Python, Matplotlib, and NumPy to calculate, visualize, print, and sample categorical probabilities.
Read how Grokking Statistics explains discrete distribution →
Related concepts
- probability distributionkind of · A probability distribution is a mathematical specification assigning probabilities to the possible values or outcomes of a random variable, with total probability one.
- Bernoulli distributionincludes · A Bernoulli distribution is a discrete probability distribution for one binary outcome, assigning probability p to one outcome and 1−p to the other.
- Poisson distributionincludes · The Poisson distribution is a discrete probability distribution that models the count of events occurring independently at a constant average rate within a fixed interval or exposure.
- continuous distributioncontrasts with · A continuous distribution is a probability distribution for a variable whose possible values form a continuous range rather than a discrete set.
- Mapimplemented by · A map is an associative data structure that stores key-value pairs and supports retrieving values by key.
- monadimplements · A monad is a functional abstraction that encapsulates computational context and provides operations for composing computations while preserving that context.
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