Glossary

Accumulator

The object that collects sufficient statistics, posterior expectations, or neural training telemetry during fitting. Accumulators are designed to be mergeable across encoded-data chunks.

Backend

The execution location for encoded data, such as local, multiprocessing, Spark, Dask, or MPI.

Capability

A declared behavior that a model supports, such as exact density, enumeration, finite support, latent posteriors, backend scoring, or conjugate updates. Prefer mixle.describe over class checks.

Composite

A distribution or estimator over tuple-shaped observations. Each tuple field has its own child distribution or estimator.

Conformal Calibration

A finite-sample calibration method used in mixle task models to form label sets and decide whether to answer locally or escalate.

Distribution

A fitted probabilistic object with parameters and scoring behavior, normally through log_density and vectorized encoded scoring.

Encoder

The object that converts Python observations into vectorized encoded data consumed by accumulators and scoring kernels.

Engine

The array, precision, or device layer used for local math, such as NumPy, Torch, JAX, or symbolic engines.

Estimator

The object that declares a model shape and knows how to estimate a fitted distribution from accumulated evidence.

Evolution Loop

The mixle.evolve measure-propose-verify-promote workflow used to improve a model while preserving an auditable anti-regression gate.

HMM

Hidden Markov model: a sequence model with a latent state path and emission distributions.

Latent Model

A model with hidden variables, such as mixture component assignments or HMM state paths.

LLMUncertainty

The mixle.reason wrapper that samples an LLM-like callable, clusters answers by meaning, computes semantic entropy, and can calibrate answer-or-abstain behavior.

Prototype Distribution

A distribution object passed to optimize as the desired model shape. Mixle derives the matching estimator from it. If the distribution’s parameter values should seed the fit, pass it as prev_estimate as well.

Operation

A transformation over distributions, such as quantization, conditioning, marginalization, projection, truncation, mixture construction, or product-of-experts pooling.

Process Model

A distribution over event histories, trajectories, partitions, or temporal arrivals, such as a Hawkes process, renewal process, birth-death process, or Chinese restaurant process.

Record

A named-field observation, usually represented as a dictionary or schema-backed record.

Relation

A ranked feasible set over structured objects, such as assignments, paths, edit-distance neighborhoods, spanning trees, feature subsets, or graph decisions.

Representation Layer

The mixle.represent subsystem that separates segmentation, embedding, heterogeneous encoding, and optional vector quantization.

Semantic Entropy

Entropy over answer meaning clusters rather than surface strings. Used as an uncertainty signal for LLM answers.

Task Model

A durable local model from mixle.task that can be loaded in a fresh process and called as a plain function.

Transformer Leaf

A neural next-token distribution used as a child in a larger mixle model, typically through TransformerLMEstimator or StreamingTransformer.