Bayesiannonparametrisen
Bayesian nonparametrics is a field within Bayesian statistics that deals with models where the complexity of the model can grow with the amount of data. Unlike traditional parametric models, which assume a fixed finite number of parameters, Bayesian nonparametric models allow for an infinite-dimensional parameter space. This means the model's structure is not predetermined but rather adapts to the data, potentially capturing more intricate patterns.
Key to Bayesian nonparametrics are probability distributions over function spaces or other infinite-dimensional objects. Common examples
The flexibility of Bayesian nonparametrics makes them suitable for a wide range of applications where complex