TensorInference.jl
TensorInference is a standalone solver written in Julia, that harnesses tensor-based technology to implement state-of-the-art algorithms for probabilistic inference in graphical models.
Package features
Solutions to the most common probabilistic inference tasks, including:
Probability of evidence (PR): Calculates the total probability of the observed evidence across all possible states of the unobserved variables.
Marginal inference (MAR): Computes the probability distribution of a subset of variables, ignoring the states of all other variables.
Maximum a Posteriori Probability estimation (MAP): Finds the most probable state of a subset of unobserved variables given some observed evidence.
Marginal Maximum a Posteriori (MMAP): Finds the most probable state of a subset of variables, averaging out the uncertainty over the remaining ones.
Why TensorInference.jl
A major challenge in developing intelligent systems is the ability to reason under uncertainty, a challenge that appears in many real-world problems across various domains, including artificial intelligence, medical diagnosis, computer vision, computational biology, and natural language processing. Reasoning under uncertainty involves calculating the probabilities of relevant variables while taking into account any information that is acquired. This process, which can be thought of as drawing global insights from local observations, is known as probabilistic inference.
Probabilistic graphical models (PGMs) provide a unified framework to perform probabilistic inference. These models use graphs to represent the joint probability distribution of complex systems in a concise manner by exploiting the conditional independence between variables in the model. Additionally, they form the foundation for various algorithms that enable efficient probabilistic inference.
However, even with the representational aid of PGMs, performing probabilistic inference remains an intractable endeavor on many real-world models. The reason is that performing probabilistic inference involves complex combinatorial optimization problems in very high dimensional spaces. To tackle these challenges, more efficient and scalable inference algorithms are needed.
As an attempt to tackle the aforementioned challenges, we present TensorInference.jl, a Julia package for probabilistic inference that combines the representational capabilities of PGMs with the computational power of tensor networks. By harnessing the best of both worlds, TensorInference.jl aims to enhance the performance of probabilistic inference, thereby expanding the tractability spectrum of exact inference for more complex, real-world models.