Bayesian Spectral Inference
Posterior autocovariances
Bayesian Spectral Inference
Computing the spectrum's posterior distribution
Compute the "empirical" spectrum of a time series.
Expectations and variances of distributions
Prior, likelihood and posterior
Filter a noisy time series for a signal of given shape
Conversion between one- and two-sided spectra
Posterior predictive sampling
Quantiles of the posterior spectrum
Posterior sampling
Compute the signal-to-noise ratio (SNR) of a signal
Tempering of (posterior) distributions
Querying the tempering parameter
Power spectral density estimation using Welch's method.
Compute windowing functions for spectral time series analysis.
Bayesian inference on the (discrete) power spectrum of time series.