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# Tutorial
- *Theoretical preliminaries*
  - [Logic Program with Annotated Disjunctions (LPAD)](tutorial/lpad.swinb)
- *Basic examples with exact inference*
  - [Coin](tutorial/inference/coin.swinb). Simple unconditional inference.
  - [Dice](tutorial/inference/dice.swinb). Conditional inference and inference with graphical results.
  - [Epidemic](tutorial/inference/epidemic.swinb). LPAD with implicit _null_ atom in the clauses, noisy or between rule groundings.
  - [Earthquake](tutorial/inference/earthquake.swinb) noisy or between different rules.
  - [Medical symptoms](tutorial/inference/sneezing.swinb) noisy or between different rules.
- *Examples with approximate inference (Monte Carlo sampling)*
  - [Coin (variant)](tutorial/inference/coinmc.swinb). Approximate inference variant of [Coin](tutorial/inference/coin.swinb).
  - [Markov chain](tutorial/inference/markov_chain.swinb) Approximate inference.
  - [Probabilistic Computation Tree Logic](tutorial/inference/pctl_slep.swinb) Approximate inference for computing expectations, mixing LPADs and Prolog.
  - [Random arithmetic functions](tutorial/inference/arithm.swinb) Conditional approximate inference with 
  rejection sampling and Metropolis-Hastings
- *Examples with structure and parameter learning*
  - [Machines](tutorial/learning/mach.swinb). Parameter learning.
  - [Registration](tutorial/learning/registration.swinb). Structure learning.
  - [Bongard](tutorial/learning/bongard.swinb). Test the learned program with graphical results.
- *More Examples with inference*
  - see [cplint inference example programs](example/inference_examples.swinb)
- *More Examples with structure and parameter learning*
  - see [cplint learning example programs](example/learning_examples.swinb)
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