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docs/src/benchmarks.md

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## Highly-correlated multivariate Guassian
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This benchmark uses [`Models.CorrelatedGaussian`](@ref) and simply measures the time it takes to fully sample down to `dlogz=0.01`. This benchmark is exactly the same as the benchmark detailed in the [JAXNS paper](https://ui.adsabs.harvard.edu/abs/2020arXiv201215286A/abstract).
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This benchmark uses [`Models.CorrelatedGaussian`](@ref) and simply measures the time it takes to fully sample down to `dlogz=0.01`. This benchmark is exactly the same as the first benchmark detailed in the [JAXNS paper](https://ui.adsabs.harvard.edu/abs/2020arXiv201215286A/abstract).
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### Timing
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```@example sample-benchmark
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using CSV, DataFrames, Plots # hide
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using CSV, DataFrames, NestedSamplers, Plots # hide
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benchdir = joinpath(dirname(pathof(NestedSamplers)), "..", "bench") # hide
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results = DataFrame(CSV.File(joinpath(benchdir, "sampling_results.csv"))) # hide
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plot(results.D, results.t, label="NestedSamplers.jl", marker=:o, yscale=:log10 # hide
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ylabel="runtime (s)", xlabel="prior dimension", leg=:topleft) # hide
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plot(results.D, results.t, label="NestedSamplers.jl", marker=:o, yscale=:log10, # hide
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ylabel="runtime (s)", xlabel="prior dimension", leg=:topleft, ylims=(1e-2, 1e4)) # hide
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```
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### Accuracy
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```@example sample-benchmark
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plot(results.D, results.dlnZ, yerr=results.lnZstd, label="NestedSamplers.jl", # hide
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marker=:o, ylabel="ΔlnZ", xlabel="prior dimension", leg=:topleft) # hide
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hlines([0.0], c=:black, ls=:dash, alpha=0.7, label="") # hide
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hline!([0.0], c=:black, ls=:dash, alpha=0.7, label="") # hide
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```

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