Predictive distribution
A predictive distribution is a probability distribution over possible values of an unobserved or future quantity, conditional on available information or inputs.
Machine Learning System Design
A probability distribution representing the possible future values of a target conditional on available information.
- How it is used
- Supports inventory and supply-chain decisions by forecasting demand distributions under a specified out-of-stock service-level agreement.
- How deep it goes
- Contrasts entire discrete distributions with point forecasts and connects asymmetry to metric choice, without deriving distribution models.
- Examples
- Uses item-store-period demand, unit sales, low-volume counts, supply-chain forecasting, and out-of-stock planning.
- What you can do after
- Evaluate forecast metrics and distinguish distributional forecasts from point forecasts for inventory decisions.
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Time Series Forecasting Using Foundation Models
A predictive distribution is a probability distribution over possible future values conditional on observed input data.
- How it is used
- Uses sampled future values to express uncertainty in probabilistic time-series forecasts.
- How deep it goes
- Explains sampling, context length, medians, and percentile intervals, but does not derive the underlying probability model.
- Examples
- Demonstrates eight-week Walmart store sales forecasts summarized by medians and 95% prediction intervals.
- What you can do after
- Apply it via Lag-Llama and Python to generate, summarize, and visualize probabilistic forecasts.
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Related concepts
- 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.
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