package prbnmcn-dagger

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Probabilistic programming library

Install

Dune Dependency

Authors

Maintainers

Sources

0.0.5.tar.gz
md5=d3d86327b3092678c431b5bc887d9dd6
sha512=4a5c781c7e20b10f7f74fa6138901acc3d9c9017a0ff1f003c913d1acbfdcae84156c004283ed656c01f7c2ca23b8cbef3a853cdfb265b64838b89fd9d5024ba

CHANGES.md.html

0.0.5

  • lmh: acceptance statistics for LMH and incremental LMH samplers

  • [Smc] uses generative samplers (type [Random.State.t -> 'a]) instead of type ['a Dist.t]

  • adapt to latest version of [prbnmcn-stats]

  • remove [samplei] function

  • smc: make [Smc.Make] functor generative (report by @nilsbecker)

  • smc: improve error message (report by @nilsbecker)

0.0.4

  • smc: domainslib-based multicore implementation

  • bugfix: Smc_inference.run_custom spawned too many particles

  • fix implicit deps (@zapashcanon)

0.0.3

API changes

  • smc: provide API for programmable resampling (review by @nilsbecker, many thanks!)

  • gsl_dist: functorize over GSL signature

    • band-aid for unavailability of gsl package in ocaml 5

    • since explicit dependency on gsl is dropped, relicensed to MIT

code quality/bug fixes

  • smc: much improved documentation (report and review by @nilsbecker)

  • removed uses of Obj.magic

  • bugfix: properly handle terminated particles in smc

    • terminated particles now continue to participate in resampling events

  • bugfix: Smc.fork off-by-one corrected

  • bugfix: Cps_monad.map_array reversed order of elements

misc

  • added gamma distribution to stats_dist

  • added kalman filter example

  • added polynomial regression example

  • added approximate bayesian computation example

  • more tests

0.0.2

  • Dependency: prbnmcn-stats.0.0.3 -> prbnmcn-stats.0.0.4

  • Add beta distribution to Gsl samplers

  • Refactor Cps monad

  • Add SMC inference

  • Simplify handler type, modularize effect definitions away from Cps_monad

  • Fix typo: bernouilli -> bernoulli (report by @nilsbecker)

0.0.1

  • First release of prbnmcn-dagger, a library for probabilistic programming

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