Probability mass function
A probability mass function assigns nonnegative probabilities to the possible values of a discrete random variable, with the probabilities summing to one.
Statistics Every Programmer Needs
A probability mass function assigns an actual probability to each possible value of a discrete random variable, with all probabilities nonnegative and summing to one.
- How it is used
- Quantifies the probability of specific discrete outcomes for modeling rolls, success counts, and event counts.
- How deep it goes
- Defines notation and probability requirements, then works through counted outcomes and distribution-specific calculations rather than deriving a universal formula.
- Examples
- Uses fair dice, two-dice totals, and Python calculations for binomial and Poisson probabilities.
- What you can do after
- Apply it via SciPy to calculate a specific binomial or Poisson probability after selecting the appropriate distribution.
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Grokking Statistics
A function that assigns a probability to each possible value of a discrete random variable.
- How it is used
- Uses exact discrete probabilities to evaluate individual outcomes and ranges in a binomial model.
- How deep it goes
- Explains parameters, SciPy usage, plotting, and range summation while deliberately avoiding combinations and permutations.
- Examples
- Demonstrates 10 trials with p=0.9, exactly 7 successes, all outcomes from 0 to 10, and 8-or-fewer cumulative probability.
- What you can do after
- Apply it via SciPy to calculate and plot exact probabilities for binomial outcomes.
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AI-Powered Search
A probability mass function assigns probabilities to the possible values of a discrete random variable.
- How it is used
- Uses token-level probabilities to identify answer boundaries in extractive question answering.
- How deep it goes
- Defines PMFs, contrasts discrete and continuous values, and applies two PMFs without deriving their mathematical training.
- Examples
- Uses a minimalist-shoes question, tokenized context, and separate start and end probability outputs.
- What you can do after
- Apply it via an extractive question-answering model to select an answer span from token-level start and end probabilities.
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Related concepts
- probability densitycontrasts with · A probability density function is a nonnegative function describing the distribution of a continuous random variable, whose integral over a region gives the probability of values in that region.
- poisson.pmfimplemented by · poisson.pmf is a SciPy statistical function that returns probability mass function values for the Poisson distribution.
- random variableused by · A random variable is a function that assigns numerical values to outcomes of a random process and is characterized by a probability distribution.
- samplingused by · Sampling is a procedure for selecting or generating observations, values, or outcomes from a larger population, signal, or probability distribution according to a specified method.
- Poisson distributionused by · 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.
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