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py_integrated_autocorrelation_times

laddu_python::extensions::callbacks

Function py_integrated_autocorrelation_times 

Source 
pub fn py_integrated_autocorrelation_times<'py>(
    py: Python<'py>,
    samples: Vec<Vec<Vec<f64>>>,
    c: Option<f64>,
) -> Bound<'py, PyArray1<f64>> ⓘ
Expand description

Calculate the integrated autocorrelation time for each parameter according to Karamanis & Beutler (2021).

§Parameters

samples : array_like An array of dimension (n_walkers, n_steps, n_parameters). c : float, default = 7.0 The time window for Sokal’s autowindowing function. If omitted, the default window size of 7.0 is used.

§Returns

array of shape (n_parameters,)

§Examples

import numpy as np from laddu import integrated_autocorrelation_times samples = np.random.randn(4, 16, 2).tolist() integrated_autocorrelation_times(samples).shape (2,)

§References

Karamanis, M. & Beutler, F. (2021). Ensemble slice sampling. Stat. Comput. 31(5). https://doi.org/10.1007/s11222-021-10038-2

Sokal, A. (1997). Monte Carlo Methods in Statistical Mechanics: Foundations and New Algorithms. NATO ASI Series, 131–192. https://doi.org/10.1007/978-1-4899-0319-8_6