fixed some bug in computation of acceptance probability in MetropolisHastings, now works with restrained value ranges too. Also changed sample_local in continuous node to direct sampling so there is no rejection sampling anymore

Had to change linear dependencies in Exponential and Beta Nodes to sigmoid functions on weighted sums. Also introduced a more complex example for Continuous Nodes

Fixed several bugs related to evidence. Hopefully included LinearBeta, still needs testing though. Changed sample_global and sample_local once again in ContinuousNode

examples/MCMC_Continuous.py läuft mit exponentialverteilung->linearGauss, Evidenz als Klassen und Kontinuierliche PoE läuft

Refactoring: ContinuousNode introduced, moved stuff from Discrete and Continuous Node so generic Node, created MCMC example for multiple continuous Models

Contstructing a MarkovChain with MetropolisHastings is now working for Networks which consist only of LinearGaussian nodes or ProbabilityTables. Additionally it is now possible to specify the form of densityfunction that one expects for PosteriorMarginal. Learning a Gaussian from a MarkovChain is also possible

ExampleComparison is running correctly, Added MetropolisHastingsAlgorithm as StateTransition and began generalization of MCMC to continuous spaces

Changed behaviour of ProbabilityTable.to_jpt_by_states back to origninal behaviour, as is documented... I dont know who changed it