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Merge pull request #208 from alecloudenback/patch-1
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Note what PT stands for
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nikola-sur authored Feb 17, 2024
2 parents b8e3599 + f32d9a9 commit 764af10
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2 changes: 1 addition & 1 deletion docs/make.jl
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Expand Up @@ -37,7 +37,7 @@ makedocs(;
),
pages=[
"Basic usage (local)" => "index.md",
"Why PT?" => "unidentifiable-example.md",
"Why parallel tempering (PT)?" => "unidentifiable-example.md",
"Parallelization" => "parallel.md",
"Distributed usage (MPI)" => "mpi.md",
"Variational PT" => "variational.md",
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4 changes: 2 additions & 2 deletions docs/src/index.md
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Expand Up @@ -16,7 +16,7 @@ of the following algorithms:

- Non-Reversible Parallel Tempering (NRPT),
[Syed et al., 2021](https://rss.onlinelibrary.wiley.com/doi/10.1111/rssb.12464).
- Variational PT, [Surjanovic et al., 2022](https://arxiv.org/abs/2206.00080).
- Variational parallel tempering (Variational PT), [Surjanovic et al., 2022](https://arxiv.org/abs/2206.00080).
- autoMALA, [Biron-Lattes et al., 2023](https://arxiv.org/abs/2310.16782).

These algorithms achieve state-of-the-art performance for approximation
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controlling the number of iterations (via `n_rounds`),
and many more options

Then, run PT (locally on one process) using the function [`pigeons()`](@ref):
Then, run parallel tempering (PT) locally on one process using the function [`pigeons()`](@ref):

```@example example
pt = pigeons(inputs);
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4 changes: 2 additions & 2 deletions docs/src/unidentifiable-example.md
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CurrentModule = Pigeons
```

# [Why PT? An example.](@id unidentifiable-example)
# [Why Parallel Tempering (PT)? An example.](@id unidentifiable-example)

Consider a Bayesian model where the likelihood is a binomial distribution with probability parameter ``p``.
Let us consider an over-parameterized model where we
Expand Down Expand Up @@ -94,4 +94,4 @@ This is also confirmed by the PT ESS estimates:

```@example why
samples
```
```

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