Baylib is a parallel inference library for discrete Bayesian networks supporting approximate inference algorithms both in CPU and GPU.

Here's a list of the main requested features:

• Copy-On-Write semantics for the graph data structure, including the conditional probability table (CPT) of each node
• parallel implementation of the algorithms either using C++17 threads or GPGPUU optimization
• GPGPU optimization implemented with opencl, using boost compute and CUDA.
• template-based classes for probability format
• input compatibility with the XDSL format provided by the SMILE library
• cmake-based deployment

• Gibbs Sampling - C++11 threads
• Likelihood Weighting - C++11 threads
• Logic Sampling - GPGPU with boost compute
• Rejection Sampling - C++11 threads
• Adaptive importance sampling - C++11 threads, GPGPU with boost compute

*It's a very well-known limitation of the Gibbs sampling approach

• cmake >= 2.8
• boost >= 1.65
• libtbb
• [optional] ocl-icd-opencl
• [optional] mesa-opencl-icd

In order to use the cuda algorithms the system must be cuda compatible and the relative cuda toolkit must be installed.

Under Linux, you can install the required dependencies using the provided script install_dependencies.sh as follows

 cd scripts/
 chmod u+x install_dependencies.sh
./install_dependencies.sh

Using the cmake FetchContent directives you can directly setup baylib as follows

include(FetchContent)

FetchContent_Declare(
        baylib
        GIT_REPOSITORY https://github.com/mspronesti/baylib.git
)

FetchContent_MakeAvailable(baylib)
# create your executable 
# and whatever you need for
# your project ...
target_link_libraries(<your_executable> baylib)

Alternatively under Linux or MacOS, you can run the provided script install.sh as follows

cd scripts/
chmod u+x install.sh
sudo ./install.sh

another option for the script is running the following commands (assuming you're in the root of the project):

mkdir build
cd build
cmake ..
make
sudo make install

You can now include baylib in your projects.

In the latter two cases, make sure your CMakeLists.txt looks like this

find_package(baylib)
# create your executable 
# and whatever you need for
# your project ...
target_link_libraries(<your_executable> baylib)

Baylib allows performing approximate inference on Bayesian Networks loaded from xdsl files or created by hand (either using named nodes or numeric identifiers).

Have a look at examples for more.

• copy-on-write
• gibbs sampling
• likelihood weighting
• likelihood weighting pseudo-code
• logic sampling
• rejection sampling pseudo-code
• adaptive importance sampling