Last updated: 2025-05-19
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Knit directory: BOSS_website/
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Unstaged changes:
Modified: BOSS_website.Rproj
Modified: analysis/co2.Rmd
Modified: analysis/mortality.Rmd
Modified: analysis/sim1.Rmd
Modified: analysis/simA1.Rmd
Modified: output/sim1/figures/Comparison Posterior Density: B = 10 .pdf
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Modified: output/sim2/BO_data_to_smooth.rda
Modified: output/sim2/BO_result_list.rda
Modified: output/sim2/rel_runtime.rda
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File | Version | Author | Date | Message |
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html | 1f3f1e6 | Ziang Zhang | 2025-05-01 | Build site. |
Rmd | 036e8eb | Ziang Zhang | 2025-05-01 | workflowr::wflow_publish("analysis/index.Rmd") |
html | 73fca34 | david.li | 2025-04-30 | Build site. |
html | 42f63a7 | david.li | 2025-04-30 | Build site. |
Rmd | 176888d | david.li | 2025-04-30 | wflow_publish("analysis/index.Rmd") |
html | c04b58d | david.li | 2025-04-30 | Build site. |
Rmd | 28db27f | david.li | 2025-04-30 | Update dimension analysis. |
html | 3f17f69 | Ziang Zhang | 2025-04-24 | Build site. |
html | ba5956f | Ziang Zhang | 2025-04-22 | Build site. |
html | 8cbef2a | Ziang Zhang | 2025-04-22 | Build site. |
html | 0f89dfc | Ziang Zhang | 2025-04-22 | Build site. |
Rmd | 22554bb | Ziang Zhang | 2025-04-22 | workflowr::wflow_publish("analysis/index.Rmd") |
html | 8e60968 | Ziang Zhang | 2025-04-21 | Build site. |
html | 5f3bf5f | Ziang Zhang | 2025-04-21 | Build site. |
Rmd | 25f71ff | Ziang Zhang | 2025-04-21 | workflowr::wflow_publish("analysis/index.Rmd") |
html | e404558 | Ziang Zhang | 2025-04-15 | Build site. |
html | 6a72e1b | Ziang Zhang | 2025-04-15 | Build site. |
Rmd | 8f72422 | Ziang Zhang | 2025-04-15 | workflowr::wflow_publish("analysis/index.Rmd") |
html | 3c4470f | Ziang Zhang | 2025-04-15 | Build site. |
Rmd | 9d10488 | Ziang Zhang | 2025-04-15 | workflowr::wflow_publish("analysis/index.Rmd") |
Rmd | c7f8fbd | Ziang Zhang | 2025-04-15 | Start workflowr project. |
For an overview of BOSS, see this paper.
Examples:
Here is a basic reproducible example of the BOSS algorithm, which illustrates the performance of the BOSS algorithm when the true posterior has different shapes.
These more advanced examples aim to provide the readers some further understanding of the BOSS algorithm and its applications, by replicating the results from the paper.
Discussion on Diagnostic of BOSS:
To guide the readers with the practical implementation of the BOSS algorithm, we provide a discussion on some possible diagnostic tools that can be used to assess the convergence of BOSS.
In addition, we also provide readers with some intuition in terms of the convergence performance of BOSS with respect to the dimension of the conditioning parameter as well as hyper-parameter \(\delta\) of the BOSS algorithm.
Background Readings:
Our implementation of the BOSS is grounded in the framework of Bayesian hierarchical models, specifically the conditional Latent Gaussian Models (cLGMs).
Approximate Bayesian inference for LGMs typically relies on the Laplace approximation and Adaptive Gauss-Hermite Quadrature (AGHQ). For readers less familiar with these concepts, we recommend the following resources:
For readers seeking a deeper understanding of Gaussian processes and the Bayesian optimization algorithm, which are central to the BOSS framework, we recommend the following books: