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<i class="bi bi-bounding-box"></i> **Graph representation**: <br>
Phasic represents Markov jump processes as graphs rather than matrices. User models are specified with a simple callback function.

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<i class="bi bi-graph-down"></i> **Moments and Distributions**: <br>
Compute PDFs and any number of exact moments for both continuous to discrete phase-type distributions.

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<i class="bi bi-rocket-takeoff"></i> **Bayesian Inference**: <br>
Parameter inference using Stein Variational Gradient Descent produce confidence intervals of estimates.

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<i class="bi bi-code-slash"></i> **Graph algorithms**: <br>
Efficient graph-based algorithms directly implemented in C with APIs for both Python, R, and C++. 
Tracing and caching of symbolic graph operations in Gaussian elimination allow evaluation of large models across parameter values.

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<i class="bi bi-cpu"></i> **Multiprocessing**: <br>
Automatically parallelizes inference computation across CPUs on a single device or multiple devices on a SLURM cluster.

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<i class="bi bi-arrow-bar-up"></i> **Distributed symbolic elimination**: <br>
Symbolic elimination traces are cached for reuse and community sharing by IPFS and a GitHub model hub.

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