Hello!
I wonder if we can retrieve iteration-level draws of random parameters in hierarchical Bayesian estimation. We do have individual-level posterior means, which should be calculated using these draws. I want to analyze the distribution of posterior/conditional means across a sample, say, \beta ...
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Search found 4 matches
- 23 Mar 2026, 17:19
- Forum: Examples (existing and requests for new ones)
- Topic: HB: Retrieving iteration-level draws of random parameters
- Replies: 1
- Views: 639
- 22 Jul 2024, 00:04
- Forum: Model specification
- Topic: HB model specification
- Replies: 5
- Views: 15049
Re: HB model specification
Thank you very much for the clarification! I'm sorry that I haven't checked the forum in a while (until IATBR 2024). I see that the HB and classical estimation are specified differently in Apollo, although both assume normally distributed random parameters.
- 13 Jun 2024, 21:52
- Forum: Model specification
- Topic: HB model specification
- Replies: 5
- Views: 15049
Re: HB model specification
Thank you for the response, Stephane! I did read the manual carefully, but I'm not familiar enough with Bayesian estimation.
Siqi
Siqi
- 13 Jun 2024, 04:41
- Forum: Model specification
- Topic: HB model specification
- Replies: 5
- Views: 15049
HB model specification
Hi!
I have a question about the hierarchical Bayes (HB) model specification. On the webpage of example files and data, Hybrid_with_OL_and_MMNL_bayesian seems to be the counterpart of Hybrid_with_OL_and_MMNL, but the former specifies a single random beta:
b_brand_Artemis = b_brand_Artemis_base ...
I have a question about the hierarchical Bayes (HB) model specification. On the webpage of example files and data, Hybrid_with_OL_and_MMNL_bayesian seems to be the counterpart of Hybrid_with_OL_and_MMNL, but the former specifies a single random beta:
b_brand_Artemis = b_brand_Artemis_base ...