We believe that probabilistic programming is going to be a cornerstone in the next machine learning revolution, and our goal is to build a community around the topic in Denmark and Sweden.
Our community is made up of theoreticians, practitioners, students, hackers, programmers, analysts, modellers and researchers from both academia and industry.
Our goals are threefold: - Foster discussions amongst people doing probabilistic programming - Present how industries use probabilistic programming in research and production - Present how academics in probabilistic programming and Bayesian reasoning are moving the field forward
This time we are lucky to host Søren Mørk and Christian Theil Have, who would like to present a gentle introduction to Probabilistic Logic Programming.
We hope to see you there! :)
Probabilistic programming is currently attracting a lot of attention. Not much of the attention, however, is directed towards the logic programming branch of probabilistic programming. Probabilistic logic programming has roots going back at least three decades. It is a disciplined approach to probabilistic programming, with strong theoretical foundations and great modeling capabilities, that is particularly well-suited for problems with discrete outcome spaces. Here, we give a general introduction to probabilistic logic programming, with examples using PRISM, a probabilistic logic programming language and machine learning system.