@@ -19,8 +19,11 @@ This is done by using Bayesian statistics and requires usually the following ste
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@@ -19,8 +19,11 @@ This is done by using Bayesian statistics and requires usually the following ste
3. Run Inference using the model and the data (e.g. by Monte Carlo Sampling) to acquire posterior distribution. In other words, the inference adjusts the prior distribution using the observed data data to give a more precise model.
3. Run Inference using the model and the data (e.g. by Monte Carlo Sampling) to acquire posterior distribution. In other words, the inference adjusts the prior distribution using the observed data data to give a more precise model.
*PPL Problem:*
*PPL Problem:*
The central problem of PPLs is that comptuting the posterior distribution of latent parameters θ and observations x<sub>1</sub>, ..., x<sub>n</sub> (i.e. p(θ|x<sub>1</sub>, ..., x<sub>n</sub>)) can be costly of even intractable.
The central problem of PPLs is that comptuting the posterior distribution of latent parameters θ and observations x<sub>1</sub>, ..., x<sub>n</sub> (i.e. p(θ|x<sub>1</sub>, ..., x<sub>n</sub>)) can be costly of even intractable.
