# 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)