kalman_smooth — MATH estimation op

• Data kinds: matrix × matrix × matrix × matrix × matrix × signal × matrix → table

• Call: import fullseye as fs; fs.ledger.kalman_smooth(z, F, H, Q, R, x0, P0) -> 'dict' (to call the implementation directly, import mathestimation; mathestimation.kalman_smooth(z, F, H, Q, R, x0, P0) -> 'dict'; from the registry, opsmath.get("kalman_smooth"))

Usage

> This operator's description has not been translated yet. The original text follows as it is.

線形ガウス状態空間モデル x_{k+1} = F x_k + w(w ~ N(0,Q))、z_k = H x_k + v(v ~ N(0,R))の

Kalman フィルタ(前向き)と Rauch–Tung–Striebel 平滑化(後ろ向き)。

`z は (T, m) の観測(NaN の行は欠測として更新を飛ばす)。返り値 {"x_filt", "P_filt", "x_smooth", "P_smooth", "nll"}`。

門: 平滑化の結果は、全観測と事前分布を一度に使う最小二乗(一括の MAP)と厳密に一致する。

Family-wide input contract (fail-closed)

Every mathops op validates its input before computing (nothing slips through silently):

• **complex input raises ValueError** — coercing to float64 silently discards the imaginary part (numpy only emits a ComplexWarning and returns a plausible-looking wrong real number). State .real/.imag/abs() explicitly, or use complexops, which handles complex data.

• **masked arrays with masked elements raise ValueError** — the implicit conversion that peels off the mask and uses the raw values underneath is refused. Say explicitly whether to fill or to drop.

• **NaN/Inf raises ValueError on every input** (refused with the count stated — it propagates through the whole result).

• Shapes are strict: 1-D and 2-D are never implicitly promoted or broadcast (a matrix in a vector slot, or a vector in a matrix slot, raises ValueError; reshape explicitly).

• Size cap: ops that take a matrix, and the stat_histogram bins, raise ValueError beyond mathops.MAX_ELEMENTS (2^26 ≈ 67 million elements).

Detailed usage guide

• math_metrology family guide

References (sample data, literature)

• Sample-data catalog (download URLs / licences) — 2-D uses skimage.data (BSD/public domain) plus synthetic images; 3-D lists download URLs for real data sources (Stanford, PDS, …).

• Operator provenance and references — the sources of the research/methods this op family came from.

• The canonical algorithm (author, year) and its uses are named in the family usage guide above.

Runnable examples (verified samples that actually call this op)

• estimation_tour — py -3.11 examples/estimation_tour.py

Ops the type connects to (they accept table as input)

dynsys_poincare_section · dwt_inverse · chebyshev_eval_nd

Same category (estimation)

assign_hungarian · hist_distance · stat_ttest_paired · stat_ttest_welch · stat_ks_test · stat_chi2_gof · crlb_gaussian · mat_expm


*Provenance: mathestimation.py — MATH operator registry. This per-op note is generated by tools/opdocs.py md (do not hand-edit).*

© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.