compute_network_marginal_probabilities produces the following crash:

terminate called after throwing an instance of 'std::future_error'
  what():  std::future_error: No associated state

The issue can be reproduced assigning element 0 and 1 to results while running on the ventureBN.xdsl network. One of the two future<shared_ptr<bcvec>> is empty despite cnmp running correctly.

Let X be a random variable of the Bayesian network. Let P be the set of parents of X. Let Y be a random variable not included in P. From basic notions of conditional probability, P( X | P U Y) = P( X | P) therefore, if a condition contains couples node-state not being actual dependencies of X, I suggest pruning the condition before discarding it (the current assertion discards it once a non-parent is detected)

The logic_sampling algorithm requires a specific format for the CPT: in the image we can see an example taken from https://repo.bayesfusion.com/bayesbox.html, the CPT must be represented as a contiguous vector, the order in which the probabilities must be reported is by column, e.g. the set of related probabilities must be contiguous, the order in which the parents is presented is very important as it will be used to interpret the CPT vector. From the image presented the derived CPT vector is: {0.8, 0.2, 0.8, 0.2, 0.8, 0.2, 0.05, 0.095} with the parents passed as: {Increased_serum_calcium, Brain_Tumor}

Our logic sampling implementation is limited mainly by how much memory we can use on the GPU, a possible problem could arise when trying to use more memory than the device has available, the solution is to simply rerun the algorithm in separate instances and accumulate the results.

• fixed typedef in adaptive_importance_sampling.hpp and logic_sampling.hpp
• added example_gpu_inference.cpp
• removed duplicate files
• removed useless import from condition_factory.hpp
• added more installation infos to README.md

boost::labeled_graph offers an easy and friendly infrastructure to better use labels on templated bayesian network.

typedef adjacency_list<
        listS,
        vecS,
        bidirectionalS,
        Vertex
> Graph;

typedef labeled_graph <Graph, Label> LabeledGraph;

First install flex and bison

sudo apt install flex
sudo apt install bison

then clone the official repository

git clone https://github.com/doxygen/doxygen.git
cd doxygen

build

mkdir build
cd build
cmake -G "Unix Makefiles" ..
make

and force a clean install of doxygen

sudo make install

Create a doc directory in the root of this project and run the following command to have a template of a doxygen configuration file

doxygen -g doxy-config

edit doxy-config with your favorite text editor (e.g. vi) to specify the value of the INPUT variable. For instance

INPUT                  = ../baylib \
                         ../baylib/graph \
                         ../baylib/inference \
                         ../baylib/network \
                         ../baylib/parser  \
                         ../baylib/probability \
                         ../baylib/tools \
                         ../baylib/tools/cow \
                         ../baylib/tools/gpu \
                         ../baylib/tools/random 

Eventually, run

doxygen doxy-config

to produce the documentation both in html and latex format.

To properly use TBB, install it with

sudo apt install libtbb-dev

(don't forget to add the above line to scripts/install_dependencies.sh). Then add the following lines to baylib/CMakeLists.txt

find_package(TBB REQUIRED)
...
set(BAYLIB_REQUIRED_LIBS
     ...
     tbb
)

When adding a new variable, the add_variable method must check whether another node already has an identical cpt. If it the case, it must be assigned through copy-on-wright facilities

2 options:

• adapt previous parser to the new bn representation
• provide a constructor for bayesian_network receiving a file to perform this task

Don't forget to add tests.

Very rarely opencl algorithms that use boost compute cause a segfault, I wasn't able to track down the leading cause as it happens with a very low probability and I didn't manage to reproduce it for now. A minimal reproducible example is needed.

I suspect I'm not using properly the following two macros:

• PYBIND11_OVERLOAD
• PYBIND11_OVERLOAD_PURE

I followed the official doc , yet it's not working