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This package provides tools to estimate standard errors and autocorrelation times of correlated time series. A typical example is a Markov chain obtained in a Metropolis Monte Carlo simulation.
Binning tools:
- Logarithmic Binning
- Size complexity:
O(log(N)) - Time complexity:
O(N)
- Size complexity:
- Full Binning (all bin sizes that work out evenly)
Statistical resampling methods:
- Jackknife resampling.
As per usual, you can install the registered package with
] add BinningAnalysisNote that there is BinningAnalysisPlots.jl which defines some Plots.jl recipes for LogBinner and FullBinner to facilitate visualizing the error convergence.
Binning tools
Logarithmic Binning
B = LogBinner()
# As per default, 2^32-1 ≈ 4 billion values can be added to the binner. This value can be
# tuned with the `capacity` keyword argument.
push!(B, 4.2)
append!(B, [1,2,3]) # multiple values at once
x = mean(B)
Δx = std_error(B) # standard error of the mean
tau_x = tau(B) # autocorrelation time
# Alternatively you can provide a time series already in the constructor
x = rand(100)
B = LogBinner(x)
Δx = std_error(B)Full Binning
B = FullBinner() # <: AbstractVector (lightweight wrapper)
push!(B, 2.0)
append!(B, [1,2,3])
x = mean(B)
Δx = std_error(B) # standard error of the mean
# Alternatively you can provide a time series already in the constructor
x = rand(100)
F = FullBinner(x)
push!(F, 2.0) # will modify x as F is just a thin wrapper
Δx = std_error(F)Resampling methods
Jackknife
x = rand(100)
xmean, Δx = jackknife(identity, x) # jackknife estimates for mean and standard error of <x>
# in this example
# isapprox(Δx, std(x)/sqrt(length(x))) == true
x_inv_mean, Δx_inv = jackknife(identity, 1 ./ x) # # jackknife estimates for mean and standard error of <1/x>
# Multiple time series
x = rand(100)
y = rand(100)
# The inputs of the function `g` must be provided as arguments in `jackknife`.
g(x, y, xy) = x * y / xy # <x><y> / <xy>
g_mean, Δg = jackknife(g, x, y, x .* y)Convenience wrapper
If you want to calculate the standard error of an existing time series there you can use the convenience wrapper std_error(x[; method=:log]). It takes a keyword argument method, which can be :log, :full, or :jackknife.
ts = rand(1000);
std_error(ts) # default is logarithmic binning
std_error(ts, method=:full)Supported types
All statistical tools should work with number-like (<: Number) and array-like (<: AbstractArray) elements. Regarding complex numbers, we follow base Julia and define
var(x) = var(real(x)) + var(imag(x)).
If you observe unexpected behavior please file an issue!
References
- J. Gubernatis, N. Kawashima, and P. Werner, Quantum Monte Carlo Methods: Algorithms for Lattice Models, Book (2016)
- V. Ambegaokar, and M. Troyer, Estimating errors reliably in Monte Carlo simulations of the Ehrenfest model, American Journal of Physics 78, 150 (2010)