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Large Scale MCMC Library\nA lightweight, modular library developed for large-scale Markov Chain Monte Carlo.\nIn particular, it implements algorithms that are infinite-dimensionally consistent to ensure discretization independent\nacceptance probabilities.\nThe library is lightweight in the sense, that it tries to keep the overhead for the user as small as possible and relies heavily on modularization, so extensions can be easily implemented.\nIt focuses on large-scale problems by supporting disk-based storage and reusing computed results as much as possible.\nIt is most likely not the fastest MCMC library out there, but it is quick to use and supports most features that are essential for MCMC on function spaces.\n\n\n### Key Features\n- **pCN and MALA support** \u003cbr\u003e\n- **On-Disk sample storage** \u003cbr\u003e\n- **Graceful exit on errors** \u003cbr\u003e\n\n\nMarkov-Chain-Monte-Carlo Methods aim to sample from an unknown probability measure $\\mu$ that is known up to a normalization constant.\nWe assume the density is known w.r.t. a reference Gaussian measure $\\mu_0 = N(0, C)$, i.e.\n\n$$\n\\frac{d\\mu}{d\\mu_0} \\propto L(u) = \\exp(-\\Phi(u)).\n$$\n\nIn this case the user only needs to supply the covariance operator of the reference measure $C$ and the potential function $\\Phi$.\n\n\n## Installation and Development\nReleases of this version can be downloaded from pip directly:\n\n```sh\npip install ls-mcmc\n```\n\nThis library relies on [uv](https://docs.astral.sh/uv/) for packaging.\nTo install a development environment, clone the Github repository and run\n\n```sh\nuv sync --all-groups\n```\n\nin the projects root folder.\n\n## Documentation\n\n### Usage\nThe usage section describes a basic setup for sampling from a distribution, configuring logging output, disk-storage and restarting of chains.\n\n### API Reference\nThe API reference contains documentation for all parts of the library.\n\n## Examples\nOther examples can be found [here](https://github.com/UQatKIT/LS-MCMC/tree/main/examples).\n\n## Acknowledgement and License\nThis package is developed by the [Uncertainty Quantification Research Group at KIT](https://www.scc.kit.edu/forschung/uq.php).\nIt is distributed under the MIT License.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fuqatkit%2Fls-mcmc","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fuqatkit%2Fls-mcmc","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fuqatkit%2Fls-mcmc/lists"}