From e48a2683cf9b0031eafaa5f88b59dedcd6cfebab Mon Sep 17 00:00:00 2001 From: "Documenter.jl" Date: Sun, 29 Dec 2024 01:37:22 +0000 Subject: [PATCH] build based on 41c6f03 --- dev/.documenter-siteinfo.json | 2 +- dev/api/index.html | 2 +- dev/index.html | 2 +- dev/internal/index.html | 2 +- dev/plotting/{25bac59b.svg => a384adae.svg} | 218 ++++++++++---------- dev/plotting/index.html | 2 +- dev/references/index.html | 2 +- 7 files changed, 115 insertions(+), 115 deletions(-) rename dev/plotting/{25bac59b.svg => a384adae.svg} (76%) diff --git a/dev/.documenter-siteinfo.json b/dev/.documenter-siteinfo.json index 9f52420e..65110ef9 100644 --- a/dev/.documenter-siteinfo.json +++ b/dev/.documenter-siteinfo.json @@ -1 +1 @@ -{"documenter":{"julia_version":"1.11.2","generation_timestamp":"2024-12-28T01:23:27","documenter_version":"1.8.0"}} \ No newline at end of file +{"documenter":{"julia_version":"1.11.2","generation_timestamp":"2024-12-29T01:37:15","documenter_version":"1.8.0"}} \ No newline at end of file diff --git a/dev/api/index.html b/dev/api/index.html index 73a3a3d9..fc021b49 100644 --- a/dev/api/index.html +++ b/dev/api/index.html @@ -41,4 +41,4 @@ x = rand(proposal, 1_000, 100) log_ratios = logpdf.(target, x) .- logpdf.(proposal, x) result = psis(log_ratios) -paretoshapeplot(result)

We can also plot the Pareto shape parameters directly:

paretoshapeplot(result.pareto_shape)

We can also use plot directly:

plot(result.pareto_shape; showlines=true)
source +paretoshapeplot(result)

We can also plot the Pareto shape parameters directly:

paretoshapeplot(result.pareto_shape)

We can also use plot directly:

plot(result.pareto_shape; showlines=true)
source diff --git a/dev/index.html b/dev/index.html index d52cd153..f5e10a25 100644 --- a/dev/index.html +++ b/dev/index.html @@ -14,4 +14,4 @@ (-Inf, 0.5] good 7 (23.3%) 959 (0.5, 0.7] okay 13 (43.3%) 938 (0.7, 1] bad 9 (30.0%) —— - (1, Inf) very bad 1 (3.3%) ——

As indicated by the warnings, this is a poor choice of a proposal distribution, and estimates are unlikely to converge (see PSISResult for an explanation of the shape thresholds).

When running PSIS with many parameters, it is useful to plot the Pareto shape values to diagnose convergence. See Plotting PSIS results for examples.

+ (1, Inf) very bad 1 (3.3%) ——

As indicated by the warnings, this is a poor choice of a proposal distribution, and estimates are unlikely to converge (see PSISResult for an explanation of the shape thresholds).

When running PSIS with many parameters, it is useful to plot the Pareto shape values to diagnose convergence. See Plotting PSIS results for examples.

diff --git a/dev/internal/index.html b/dev/internal/index.html index a8bf176b..e9c9ada4 100644 --- a/dev/internal/index.html +++ b/dev/internal/index.html @@ -1,2 +1,2 @@ -Internal · PSIS.jl
GitHub

Internal

PSIS.GeneralizedPareto — Type
GeneralizedPareto{T<:Real}

The generalized Pareto distribution.

Constructor

GeneralizedPareto(μ, σ, k)

Construct the generalized Pareto distribution (GPD) with location parameter $μ$, scale parameter $σ$ and shape parameter $k$.

Note

The shape parameter $k$ is equivalent to the commonly used shape parameter $ξ$. This is the same parameterization used by Vehtari et al. [1] and is related to that used by Zhang and Stephens [2] as $k \mapsto -k$.

source
PSIS.fit_gpd — Method
fit_gpd(x; μ=0, kwargs...)

Fit a GeneralizedPareto with location μ to the data x.

The fit is performed using the Empirical Bayes method of Zhang and Stephens [2].

Keywords

  • prior_adjusted::Bool=true, If true, a weakly informative Normal prior centered on $\frac{1}{2}$ is used for the shape $k$.
  • sorted::Bool=issorted(x): If true, x is assumed to be sorted. If false, a sorted copy of x is made.
  • min_points::Int=30: The minimum number of quadrature points to use when estimating the posterior mean of $\theta = \frac{\xi}{\sigma}$.

References

  • [2] Zhang & Stephens, Technometrics 51:3 (2009)
source
+Internal · PSIS.jl
GitHub

Internal

PSIS.GeneralizedPareto — Type
GeneralizedPareto{T<:Real}

The generalized Pareto distribution.

Constructor

GeneralizedPareto(μ, σ, k)

Construct the generalized Pareto distribution (GPD) with location parameter $μ$, scale parameter $σ$ and shape parameter $k$.

Note

The shape parameter $k$ is equivalent to the commonly used shape parameter $ξ$. This is the same parameterization used by Vehtari et al. [1] and is related to that used by Zhang and Stephens [2] as $k \mapsto -k$.

source
PSIS.fit_gpd — Method
fit_gpd(x; μ=0, kwargs...)

Fit a GeneralizedPareto with location μ to the data x.

The fit is performed using the Empirical Bayes method of Zhang and Stephens [2].

Keywords

  • prior_adjusted::Bool=true, If true, a weakly informative Normal prior centered on $\frac{1}{2}$ is used for the shape $k$.
  • sorted::Bool=issorted(x): If true, x is assumed to be sorted. If false, a sorted copy of x is made.
  • min_points::Int=30: The minimum number of quadrature points to use when estimating the posterior mean of $\theta = \frac{\xi}{\sigma}$.

References

  • [2] Zhang & Stephens, Technometrics 51:3 (2009)
source
diff --git a/dev/plotting/25bac59b.svg b/dev/plotting/a384adae.svg similarity index 76% rename from dev/plotting/25bac59b.svg rename to dev/plotting/a384adae.svg index 015036e0..32548695 100644 --- a/dev/plotting/25bac59b.svg +++ b/dev/plotting/a384adae.svg @@ -1,124 +1,124 @@ - + - + - + - + - + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/dev/plotting/index.html b/dev/plotting/index.html index eaf330c8..3a784b98 100644 --- a/dev/plotting/index.html +++ b/dev/plotting/index.html @@ -11,4 +11,4 @@ (-Inf, 0.5] good 4 (20.0%) 959 (0.5, 0.7] okay 9 (45.0%) 938 (0.7, 1] bad 7 (35.0%) ——

Plots.jl

PSISResult objects can be plotted directly:

using Plots
-plot(result; showlines=true, marker=:+, legend=false, linewidth=2)
Example block output

This is equivalent to calling PSISPlots.paretoshapeplot(result; kwargs...).

+plot(result; showlines=true, marker=:+, legend=false, linewidth=2)Example block output

This is equivalent to calling PSISPlots.paretoshapeplot(result; kwargs...).

diff --git a/dev/references/index.html b/dev/references/index.html index c36a0037..f3e16ba4 100644 --- a/dev/references/index.html +++ b/dev/references/index.html @@ -1,2 +1,2 @@ -References · PSIS.jl
GitHub
+References · PSIS.jl
GitHub