Bayesian model
A Bayesian model represents uncertain quantities with prior distributions, likelihoods, and posterior distributions updated using observed data.
Bayesian Optimization in Action
A Bayesian model represents uncertain quantities with probability distributions and updates beliefs using observed data and Bayes' theorem.
- How it is used
- Updates beliefs about uncertain claims after observing data, including repeated successful predictions.
- How deep it goes
- Defines prior, likelihood, and posterior through a toy example while treating Bayes’ theorem only at a high level.
- Examples
- Uses a carnival psychic guessing secret digits one hundred times and compares three observers’ reactions.
- What you can do after
- Evaluate how prior beliefs influence posterior conclusions when multiple observers receive identical evidence.
Read how Bayesian Optimization in Action explains Bayesian model →
Causal AI
Bayesian modeling is the construction and analysis of probabilistic models using prior distributions, likelihoods, and posterior distributions.
- How it is used
- Handles uncertainty when causal assumptions and level 1 and 2 data do not identify a counterfactual query.
- How deep it goes
- Specifies a prior-to-posterior SCM workflow and sampling procedure, without detailing posterior computation or a general identification algorithm.
- Examples
- Uses a promotion-retention case involving persuadable, lost-cause, and sleeping-dog customers.
- What you can do after
- Evaluate/understand counterfactual uncertainty by sampling plausible SCMs after updating them with observational and interventional data.
Related concepts
- language modelcontrasts with · A language model is a computational model that represents patterns in linguistic tokens to estimate sequence probabilities and predict or generate language from context.
- prior probabilityhas part · A prior probability distribution represents beliefs or assumptions about an unknown quantity before relevant observations or evidence are incorporated.
- likelihoodhas part · A likelihood is the probability of observed data under a specified probabilistic model, regarded as a function of candidate hypotheses or parameter values.
- posterior probabilityproduces · A posterior probability or distribution expresses the probability assigned to an event, hypothesis, or unknown quantity conditional on observed evidence and prior information.
- datasethas part · A dataset is an organized collection of related data items, records, or observations treated as a unit for storage, processing, analysis, or computation.
- prior knowledgeuses · Prior knowledge is information, beliefs, skills, or assumptions available to an agent before it encounters a task or incorporates new observations.
These guides are generated from the text of Manning books by Manning's knowledge graph and link to the sections they draw on. The full books are available on liveBook and at manning.com.