markovlib

Markov-like processes as one recursion over a belief algebra and a semiring — HMM, HSMM, Kalman, and particle inference behind a decidable engine dispatch.

markovlib is a small, typed library for Markov-like processes — hidden Markov models, semi-Markov (explicit-duration) models, linear-Gaussian state-space models, and general nonlinear ones. Its one idea: each of these is a factor graph with repeated temporal structure, and every algorithm — filter, smooth, decode, learn — is a query that resolves to a message-passing engine over a belief algebra. Swap the belief (categorical → Gaussian → particle) or the semiring (sum-product → max-plus) and the same recursion becomes forward–backward, the Kalman filter, or Viterbi.

import numpy as np
import markovlib as mk

model  = mk.DiscreteChain(np.log([0.6, 0.4]), np.log([[0.7, 0.3], [0.2, 0.8]]))
log_em = np.log([[.9, .1], [.8, .2], [.1, .9], [.2, .8], [.6, .4]])  # (T, S) log p(obs | state) — you supply this

post = mk.smooth(model, log_em)   # forward–backward: posterior marginals + log-likelihood
path = mk.decode(model, log_em)   # Viterbi: the MAP state path  ->  [0, 0, 1, 1, 1]
mk.fit(model, log_em)             # Baum–Welch: learn the dynamics (loglik never decreases)

Why markovlib

• One recursion, many algorithms. A single belief-generic forward recursion is the primitive. With a categorical belief it is forward–backward; with a Gaussian belief it is the Kalman filter. Swap the semiring from sum-product to max-plus and posteriors become the MAP path. Inference, decoding, and learning are three queries over one model, not three codebases.
• Decidable dispatch — never a silent approximation. resolve_engine(model, query) returns evidence: Exact, Approximate (carrying the method and error character, e.g. O(1/√N) Monte Carlo), or Intractable (carrying a reason). The exact engines (HMM, HSMM, Kalman) say so; the particle filter honestly says it is approximate. Nothing guesses behind your back.
• Exact where it can be. Finite chains get exact forward–backward / Viterbi / EM; linear-Gaussian models get the exact Kalman filter and RTS smoother; explicit-duration models get the standard right-censored EDHMM. Approximation (the particle filter) is opt-in and labelled.
• Reproducible randomness. The particle filter is a deterministic function of its seed — the seed is an explicit input, so a run is exactly reproducible.
• Typed, tested, tiny. numpy + scipy only, mypy --strict, and a 100% coverage gate. Every engine is validated against an independent oracle (brute-force enumeration or a textbook reference).

Install

pip install marmo

Published on PyPI as marmo (the name markovlib was already taken); the import package is markovlib — import markovlib. Requires Python 3.12+; numpy and scipy come with it.

From a checkout, with the dev extras (ruff, mypy, pytest):

git clone https://github.com/ryanrudes/markovlib && cd markovlib
uv pip install -e '.[dev]'

The engines, at a glance

You build a model, supply your per-step evidence, then run a query. For discrete chains the evidence is a (T, S) log-emission matrix — log p(observation_t | state = s) — which you compute from your own observation model. That matrix is the seam: markovlib never sees your raw data.

Transitions may be homogeneous (S, S) or time-varying (T-1, S, S). Durations are NegBinomDuration(mean, concentration) (the concentration → 1 limit is a plain HMM's geometric dwell).

A taste — each model in two lines

# Semi-Markov: dwell is modelled, so 1-frame blips are intrinsically improbable.
hsmm = mk.SemiMarkovChain(log_init, log_trans,
                          durations=(mk.NegBinomDuration(5, 4), mk.NegBinomDuration(3, 4)), max_duration=12)
mk.decode(hsmm, log_em)        # explicit-duration MAP path

# Kalman filter + RTS smoother over a linear-Gaussian model.
lg = mk.LinearGaussian(A, Q, H, R, m0, P0)
mk.filter(lg, observations)    # FilterResult(means, covariances, loglik)
mk.smooth(lg, observations)    # RTS-smoothed estimates

# Particle filter over a general nonlinear model (deterministic given the seed).
ssm = mk.StateSpaceModel(sample_prior, propagate, log_likelihood)
mk.particle_filter(ssm, observations, n_particles=5000, seed=0)

Examples

Runnable, commented scripts in examples/ — each is exercised by the test suite, so they stay current:

python examples/01_hmm_quickstart.py

Learn more

• docs/design.md — the design: a Markov-like process as a factor graph with repeated temporal structure; the three unifications (one recursion × semiring; the engine family; learning via expected sufficient statistics); and the decidable engine dispatch.
• The API is small and discoverable from markovlib.__all__; every public symbol is typed.

Development

uv pip install -e '.[dev]'
pytest --cov=markovlib   # tests + 100% coverage gate
ruff check . && ruff format --check .
mypy                     # strict

CI runs all four on every push (Python 3.12 and 3.13) and validates that the package builds; the same checks are available as pre-commit hooks (pre-commit install && pre-commit install --hook-type pre-push). Releases are tag-driven and publish to PyPI via Trusted Publishing — see RELEASING.md.

License

Apache-2.0.