Probabilistic model
A probabilistic model represents uncertainty by specifying probability distributions over variables, observations, events, or outcomes.
Causal AI
Probabilistic modeling is the construction of models that represent uncertainty and data-generating processes with probability distributions.
- How it is used
- Deploys probability distributions to represent uncertainty, simulate data, answer queries, and support statistical or causal inference.
- How deep it goes
- Explains the query-to-inference workflow, visualizes distributions, and implements inference, while stopping short of a mathematically exhaustive introduction.
- Examples
- Uses MNIST digit classification, distribution plots, transport-and-education inference, and a probabilistic enzyme model.
- What you can do after
- Apply it via pgmpy or Pyro to specify probabilistic models and infer conditional outcome distributions.
Time Series Forecasting Using Foundation Models
A probabilistic model represents uncertainty by assigning probability distributions to possible data or prediction outcomes.
- How it is used
- Uses conditional outcome distributions to quantify uncertainty in time-series forecasts.
- How deep it goes
- Defines distributions, intervals, sampling, and tokenization, but defers detailed probability theory and formal training mathematics.
- Examples
- Demonstrates TimeGPT, Lag-Llama, Chronos, and Moirai, including Student’s t-distributions, tokenization, samples, and median forecasts.
- What you can do after
- Apply it via forecasting models to obtain prediction intervals or derive point forecasts such as medians from predictive samples.
Read how Time Series Forecasting Using Foundation Models explains probabilistic model →
Related concepts
- TimeGPTincludes · TimeGPT is a proprietary foundation model designed for time-series forecasting and related time-series analysis.
- Moiraiincludes · Moirai is a foundation model designed for time-series forecasting across varied datasets.
- point forecastcontrasts with · A point forecast is a prediction that assigns a single value to each future observation without representing forecast uncertainty.
- language modelincludes · A language model is a computational model that represents patterns in linguistic tokens to estimate sequence probabilities and predict or generate language from context.
- Bayesian networkincludes · A Bayesian network is a probabilistic graphical model whose directed acyclic graph represents random variables and their conditional dependencies.
- Lag-Llamaincludes · Lag-Llama is a foundation model designed for probabilistic time-series forecasting using lagged series values and date-time features.
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