We lifted 12 functions out of this paper's own repositories and ran 12 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| chau999/credaltwosampletests | canonical | 12 of 12 |
| Function | Status | Where it lives |
|---|---|---|
| build_credal_set_for_equality_test | Ran | chau999/credaltwosampletests/src/experiment_scripts/mnist/utils.py code served (permissive licence) · get_code("3519e536f087a4e7") |
| compute_median_heuristic | Ran | chau999/credaltwosampletests/src/testing_algorithms/kernels.py code served (permissive licence) · get_code("da3dc2c1d06bb051") |
| compute_p_value | Ran | chau999/credaltwosampletests/src/testing_algorithms/utils.py code served (permissive licence) · get_code("67793f19e4bbcca1") |
| compute_root_split_ratio | Ran | chau999/credaltwosampletests/src/testing_algorithms/utils.py code served (permissive licence) · get_code("5fb09a4c4b7ad3f4") |
| generate_corner_gaussian_samples | Ran | chau999/credaltwosampletests/src/simulation/multivariate_gaussian.py code served (permissive licence) · get_code("a01c789865a5f3b0") |
| generate_corner_student_samples | Ran | chau999/credaltwosampletests/src/simulation/multivariate_student.py code served (permissive licence) · get_code("419273f47d5d8719") |
| generate_mixture_samples | Ran | chau999/credaltwosampletests/src/simulation/utils.py code served (permissive licence) · get_code("28ef5376825911f6") |
| gram_matrix | Ran | chau999/credaltwosampletests/src/testing_algorithms/kernels.py code served (permissive licence) · get_code("2207e210bb87bb78") |
| initialise_moments_on_circle | Ran | chau999/credaltwosampletests/src/simulation/utils.py code served (permissive licence) · get_code("894cfac67426e87a") |
| load_digit | Ran | chau999/credaltwosampletests/src/experiment_scripts/mnist/utils.py code served (permissive licence) · get_code("1bf858654629cec6") |
| runif_in_simplex | Ran | chau999/credaltwosampletests/src/simulation/utils.py code served (permissive licence) · get_code("8f9369b0b19419f5") |
| trim_input | Ran | chau999/credaltwosampletests/src/testing_algorithms/mmdq.py code served (permissive licence) · get_code("b0e7d674d1f93035") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
We introduce credal two-sample testing, a new hypothesis testing framework for comparing credal sets -- convex sets of probability measures where each element captures aleatoric uncertainty and the set itself represents epistemic uncertainty that arises from the modeller's partial ignorance. Compared to classical two-sample tests, which focus on comparing precise distributions, the proposed framework provides a broader and more versatile set of hypotheses. This approach enables the direct integration of epistemic uncertainty, effectively addressing the challenges arising from partial ignorance in hypothesis testing. By generalising two-sample test to compare credal sets, our framework enables reasoning for equality, inclusion, intersection, and mutual exclusivity, each offering unique insights into the modeller's epistemic beliefs. As the first work on nonparametric hypothesis testing for comparing credal sets, we focus on finitely generated credal sets derived from i.i.d. samples from multiple distributions -- referred to as credal samples. We formalise these tests as two-sample tests with nuisance parameters and introduce the first permutation-based solution for this class of problems, significantly improving existing methods. Our approach properly incorporates the modeller's epistemic uncertainty into hypothesis testing, leading to more robust and credible conclusions, with kernel-based implementations for real-world applications.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2410.12921")
get_code_for_paper("2410.12921")
have("2410.12921")
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