abcel
Empirical Likelihood-Based Approximate Bayesian Computation
Empirical likelihood-based approximate Bayesian Computation. Approximates the required posterior using empirical likelihood and estimated differential entropy. This is achieved without requiring any specification of the likelihood or estimating equations that connects the observations with the underlying parameters. The procedure is known to be posterior consistent. More details can be found in Chaudhuri, Ghosh, and Kim (2024) doi:10.1002/SAM.11711.
- Version1.0
- R versionunknown
- LicenseGPL-2
- Needs compilation?Yes
- Last releaselast Friday at 12:00 AM
Team
Sanjay Chaudhuri
MaintainerShow author detailsNicholas Chua
Show author detailsRolesAuthorRiddhimoy Ghosh
Show author detailsRolesAuthor
Insights
Last 30 days
The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.
Last 365 days
The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.
Data provided by CRAN