mc_sequential_mean — CONVERGE `montecarlo` op

• 資料種類:無 → table(僅由參數決定的運算子 —— 不接受影像或資料輸入)

• 呼叫: import fullseye as fs; fs.ledger.mc_sequential_mean(sampler='pi', tol=0.01, rel_tol=0.0, confidence=0.95, batch=4096, max_samples=4000000, seed=0, on_fail='raise') -> 'dict'(要直接呼叫實作,import converge; converge.mc_sequential_mean(sampler='pi', tol=0.01, rel_tol=0.0, confidence=0.95, batch=4096, max_samples=4000000, seed=0, on_fail='raise') -> 'dict';從台帳取用則 opsconverge.get("mc_sequential_mean"))

用法

序貫蒙地卡羅平均：持續加樣本，直到信賴區間窄於容許值。

> 以下的詳細說明為原文 —— 摘要與標題已翻譯。

sampler(rng, n) returns n i.i.d. samples (a 1-D array) — or the built-in "pi" (4 * [u^2 + v^2 <= 1], mean pi). Batch b is drawn from default_rng((seed, b)), so the result is reproducible and does not depend on how many batches came before.

Stops at the first batch boundary, from the second batch on, where the half-width z * s / sqrt(n) <= tol + rel_tol * |mean| (z from confidence; s the running sample standard deviation). This is the fixed-width sequential interval of Chow & Robbins 1965; never stopping inside the first two batches guards against an early, accidentally small variance (raise batch to guard more) — the actual coverage is checked by the gates, not assumed.

Returns mean, se, ci (lower, upper), half_width, n_samples (used), converged and history (n, mean, se per batch). Refusal: max_samples reached first raises NotConvergedError (on_fail="record" returns the record with mean = None and the running values under last_mean / last_se).

詳細使用指南

• convergent_computation 族使用指南

參考(範例資料、文獻)

• 範例資料目錄(下載 URL / 授權) —— 2-D 用 skimage.data(BSD/公有領域)加合成圖,3-D 給出真實資料源(Stanford/PDS 等)的下載 URL。

• 運算子來歷與參考文獻 —— 該運算子族所依據的研究/方法出處。

• 演算法的正典(作者、年份)與用途見上面的族使用指南。

可執行的範例(實際呼叫該運算子並已驗證的樣例)

• (尚無)

型別可銜接的下一個運算子(可接受 `table` 作為輸入)

—

同類別(`montecarlo`)

mc_photon_slab

*Provenance: converge.py — CONVERGE 運算子登記表。本條目由 tools/opdocs.py md 自動產生(請勿手動編輯)。*

mc_sequential_mean — CONVERGE montecarlo op

用法