Open Issues Need Help
View All on GitHubAI Summary: Enhance the 'pykelihood' package to handle n-dimensional covariates in kernel functions. This involves addressing broadcasting issues for multi-dimensional inputs and potentially allowing users to specify covariates using named dataframes (similar to R's syntax). The solution needs to consider how SciPy handles n-dimensional parameters and ensure compatibility.
package useful for likelihood-based inference
AI Summary: Implement a feature in the `pykelihood` Python package to handle Bayesian inference when a distribution is a parameter of another distribution. This involves automatically generating a PyMC model and performing MCMC optimization.
package useful for likelihood-based inference
AI Summary: Implement support for sparse parameters within the pykelihood package. This involves enabling the use of sparse arrays for parameters and providing mechanisms to optimize only specific elements within multi-dimensional parameters, potentially through a flexible Parameter-protocol or case-by-case handling.
package useful for likelihood-based inference
AI Summary: Implement parameter domain constraints for improved robustness in the pykelihood package. This involves adding metadata for parameter bounds, potentially using constraint-aware optimization algorithms (like COBYQA), or incorporating penalties for out-of-domain values, especially handling complex, multi-dimensional parameter relationships.
package useful for likelihood-based inference