```
tags:
- AI/Algorithms/BayesModel
- AI/ML/SupervisedLearning
- Statistics/Bayes
aliases:
- Bayesian Non-Parametrics
- BNP
```

Bayesian Non-Parametrics (BNP) is a Statistical approach within Bayesian inference that utilizes infinitely dimensional objects like functions or probability distributions as priors. This allows for remarkable flexibility and avoids the need for pre-specifying a rigid model structure.

BNP is a powerful framework for situations where the underlying data distribution is unknown or complex. However, the computational demands and theoretical underpinnings can be challenging for beginners.

Notes:

- BNP models employ priors that are not limited to a fixed number of parameters, but rather can adapt to the complexity of the data (Infinite-Dimensional Priors).
- Unlike traditional parametric models with a set number of parameters, BNP models offer greater flexibility and are less reliant on strong prior assumptions.
- BNP models often center around priors that represent probability distributions themselves, I.e. focusing on random probability measures, allowing the data to "speak for itself" in determining the underlying distribution.
- Dirichlet Process (DP) is a widely used BNP model for mixture modeling, while Gaussian Process Priors (GPP) are another example for continuous data.
- Inference in BNP models can be computationally intensive compared to simpler models due to the inherent complexity of infinite-dimensional objects.

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