Refresh README, LICENSE, and NOTICE metadata#446
Conversation
Standardize the README to match the structure/style/badges used across other CliMA repos (logo, badge table, Features, Quick Example), and update the LICENSE/NOTICE copyright line to reflect the repo's actual first-commit year (2019-2026), California Institute of Technology (Climate Modeling Alliance). Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01T3GJ3g8X3sQuPCnGcrwfTy
Codecov Report✅ All modified and coverable lines are covered by tests. Additional details and impacted files@@ Coverage Diff @@
## main #446 +/- ##
==========================================
- Coverage 95.16% 95.04% -0.13%
==========================================
Files 12 12
Lines 2441 2441
==========================================
- Hits 2323 2320 -3
- Misses 118 121 +3 ☔ View full report in Codecov by Harness. 🚀 New features to boost your workflow:
|
| [zenodo-url]: https://zenodo.org/badge/latestdoi/179573047 | ||
|
|
||
| # Quick links | ||
| `CalibrateEmulateSample.jl` solves parameter estimation problems using accelerated (and approximate) Bayesian inversion. Given an observation `y`, a computer model (forward map) `G`, observational noise covariance `Γ`, and broad prior information on the parameters `θ`, it returns the joint data-informed distribution of "`θ` given `y`" for `y = G(θ) + η`, `η ~ N(0, Γ)`. The framework is not inherently restricted to unimodal distributions and can be applied to computer models that are noisy or chaotic, non-differentiable, or that can only be treated as a black box (interfaced only through parameter files). As the name suggests, the package breaks the problem into three stages — calibration (via [EnsembleKalmanProcesses.jl](https://github.com/CliMA/EnsembleKalmanProcesses.jl)), emulation of the forward map, and sampling of the emulated posterior — following the approach described in [Cleary et al. (2020)](https://arxiv.org/pdf/2001.03689.pdf). |
There was a problem hiding this comment.
How about:
CalibrateEmulateSample.jl solves parameter estimation problems using machine-learning accelerated (and approximate) Bayesian inversion, originally described in Cleary et al. (2020). In simple terms, "Calibrate, Emulate, Sample" (CES) provides an approximate joint parameter distributions of dimension upto ~100s with only hundreds of simulator evaluations. The framework is not inherently restricted to unimodal distributions, and particularly applicable to computer models that are noisy or chaotic, non-differentiable, or that can only be treated as a black box.
To get started we recommend first calibrating a model (without uncertainty) with the toolkit EnsembleKalmanProcesses.jl, then CalibrateEmulateSample.jl post-processes the results (with machine-learnt tools and Markov Chain Monte Carlo) to get uncertainty quantification from this.
|
|
||
| ## Features | ||
|
|
||
| - **Derivative-free Bayesian inversion**: learns the joint distribution over parameters `θ` given noisy observations `y = G(θ) + η`, without requiring gradients of the forward model `G`. |
There was a problem hiding this comment.
This is fine. but I would add that \eta is "additive Gaussian noise"
Reduces vertical space while keeping row labels, pairing badges into a 4-column table instead of one row per category. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01T3GJ3g8X3sQuPCnGcrwfTy
| | **License** | [![license][license-img]][license-url] | **Docs Build** | [![docs build][docs-bld-img]][docs-bld-url] | | ||
| | **Tests** | [![unit tests][unit-tests-img]][unit-tests-url] | **Code Coverage** | [![codecov][codecov-img]][codecov-url] | | ||
| | **Downloads** | [![Downloads][dlt-img]][dlt-url] | **JOSS** | [![DOI][joss-img]][joss-url] | | ||
| | **DOI** | [![zenodo][zenodo-img]][zenodo-url] | | | |
There was a problem hiding this comment.
not rendering correctly
| - **Derivative-free Bayesian inversion**: learns the joint distribution over parameters `θ` given noisy observations `y = G(θ) + η`, without requiring gradients of the forward model `G`. | ||
| - **Works with black-box forward models**: compatible with computer models that are noisy, chaotic, non-differentiable, or only interfaced through parameter files. | ||
| - **Three-stage pipeline**: *Calibrate* with [EnsembleKalmanProcesses.jl](https://github.com/CliMA/EnsembleKalmanProcesses.jl), *Emulate* the forward map with a Gaussian Process or Random Feature interface, and *Sample* the emulated posterior with Markov chain Monte Carlo. | ||
| - **Flexible emulators**: Gaussian Process and Random Feature emulator interfaces, with configurable kernel structures and input/output data encoding and decorrelation schedules. |
There was a problem hiding this comment.
remove: "and input/output data encoding and decorrelation schedules."
2 participants
Summary
.ctypo in the downloads-badge URL and corrected the codecov badge branch frommastertomain(verifiedmainis the actual default branch).2019-2026 California Institute of Technology (Climate Modeling Alliance), using the repo's actual first-commit year.Test plan
🤖 Generated with Claude Code
https://claude.ai/code/session_01T3GJ3g8X3sQuPCnGcrwfTy