I used this one summer to implement a graphical model I was working with. It has two approximate inference algorithms: variational message passing and expectati

I used this one summer to implement a graphical model I was working with. It has two approximate inference algorithms: variational message passing and expectation propagation, for which the inventors are co-authors of this library. I found the library to be powerful for those who knew what they were doing. But it took me a while to figure out the syntax. Some bits were a bit hack-ey which made the syntax difficult for me to get right. For example, random variables are templates that take a bool or float depending on the underlying distribution. You could have a C# array of random variables, or a infer.net array of random variables, which had slightly different functionality. The infer.net array of random variables did not use the C# for loop, and instead had its own for loop implemented as a method. Anyways, it's a nice library and great that they released the source code.