Yes, you can Google the key words to find the BUGS ( including WinBUGS and OpenBUGS) codes for your model but usually they will not work or you just can not understand what they are writing. You should firstly understand basic codes skill about BUGS:

1. Process for Building Bayesian Model

You could refer to the following picture.

see the example in the book,Applied Bayesian Statistics, pp.147

2. BUGS code structure

"Data analysis using regression and multilevel/hierarchical models" (section 16.8, p.366) gives excellent explanations about the BUGS code structure.

3. How to perform the codes

"Bayesian Modeling Using WinBUGS" (chapter 3 and 4) gives clear pictures to teach you how to run your codes.

4. Run BUGS through R

"Simple linear regression using R2OpenBUGS"

It is not easy to import your data in BUGS if your dataset is too big. Try to use R as a tool to manage data and deal with the MCMC results. This author offers a simple example (linear model) to teach you how to use these. If you want to totally include your BUGS code in R console, you should use sink and cat function , see this blog for a very vivid example. Other useful tricks for running BUGS within R could also found in book, Introduction to WinBUGS for Ecologists, (Appendix 1 - A List of WinBUGS Tricks).

5. Other special BUGS functions

Useful functions could be found here. More examples about using these should refer to The BUGS Book.

6. Bayes' Theory

I have gave a reading list for understanding this part in my another post. You should at least fully understand the first chapter of Bayesian Analysis for the Social Sciences.

Now you understand the basics, and you could try to search the codes about your interested models. The best and workable sources come from the books and journal articles. Most authors below are very generous to provide the codes in their website. I will not provide these codes here but will suggest the references for you.

1. Efficiency estimation

(1)Stochastic frontier model

"Bayesian stochastic frontier analysis using WinBUGS"

The author (Professor Jim Griffin) provides the WinBUGS code in his website.

I depict their data structure as follows and you should also read their paper on page 165.

2. Panel data analysis

"Introduction to applied Bayesian statistics and estimation for social scientists" (ch.9)

Relevant theory of Bayesian panel data model could refer to Bayesian Econometrics (ch.7), Panel Data Modeling and Inference: A Bayesian Primer, Introduction to Modern Bayesian Econometrics (ch.7)

For specific panel data analysis demonstration:

3. Multilevel/hierarchical models

"Data analysis using regression and multilevel/hierarchical models" especially chapter 17, p.305

"Applied Bayesian Hierarchical Methods"

"Scope economies in Australian distance education"

4. Econometrics

"An introduction to modern Bayesian Econometrics"

5. Rasch (IRT) Model

"Bayesian Estimation for the Rasch Model using WinBUGS"

6. Random Coefﬁcient Dynamic Factor Models

"Bayesian Estimation of Random Coefﬁcient Dynamic Factor Models"

7. Categorical models

Bayesian Models for Categorical Data, codes

8. Latent Growth Model

latent basis growth curve model with varying residual variances

latent basis growth curve model

9. Prediction

Linear regression: Bayesian Ideas and Data Analysis p. 241

Other models

"The BUGS Book"

"Bayesian modeling using WinBUGS"

This article will continuously be updated and I welcome anyone of you to post your experience here.

1. Process for Building Bayesian Model

You could refer to the following picture.

Process for Building Bayesian Model |

2. BUGS code structure

"Data analysis using regression and multilevel/hierarchical models" (section 16.8, p.366) gives excellent explanations about the BUGS code structure.

3. How to perform the codes

"Bayesian Modeling Using WinBUGS" (chapter 3 and 4) gives clear pictures to teach you how to run your codes.

4. Run BUGS through R

"Simple linear regression using R2OpenBUGS"

It is not easy to import your data in BUGS if your dataset is too big. Try to use R as a tool to manage data and deal with the MCMC results. This author offers a simple example (linear model) to teach you how to use these. If you want to totally include your BUGS code in R console, you should use sink and cat function , see this blog for a very vivid example. Other useful tricks for running BUGS within R could also found in book, Introduction to WinBUGS for Ecologists, (Appendix 1 - A List of WinBUGS Tricks).

5. Other special BUGS functions

Useful functions could be found here. More examples about using these should refer to The BUGS Book.

6. Bayes' Theory

I have gave a reading list for understanding this part in my another post. You should at least fully understand the first chapter of Bayesian Analysis for the Social Sciences.

Now you understand the basics, and you could try to search the codes about your interested models. The best and workable sources come from the books and journal articles. Most authors below are very generous to provide the codes in their website. I will not provide these codes here but will suggest the references for you.

1. Efficiency estimation

(1)Stochastic frontier model

"Bayesian stochastic frontier analysis using WinBUGS"

The author (Professor Jim Griffin) provides the WinBUGS code in his website.

I depict their data structure as follows and you should also read their paper on page 165.

2. Panel data analysis

"Introduction to applied Bayesian statistics and estimation for social scientists" (ch.9)

Relevant theory of Bayesian panel data model could refer to Bayesian Econometrics (ch.7), Panel Data Modeling and Inference: A Bayesian Primer, Introduction to Modern Bayesian Econometrics (ch.7)

For specific panel data analysis demonstration:

- random coefficient model or varying-coefficients model: Bayesian Econometric Methods (ch. 12)

3. Multilevel/hierarchical models

"Data analysis using regression and multilevel/hierarchical models" especially chapter 17, p.305

"Applied Bayesian Hierarchical Methods"

"Scope economies in Australian distance education"

4. Econometrics

"An introduction to modern Bayesian Econometrics"

5. Rasch (IRT) Model

"Bayesian Estimation for the Rasch Model using WinBUGS"

6. Random Coefﬁcient Dynamic Factor Models

"Bayesian Estimation of Random Coefﬁcient Dynamic Factor Models"

7. Categorical models

Bayesian Models for Categorical Data, codes

8. Latent Growth Model

latent basis growth curve model with varying residual variances

latent basis growth curve model

9. Prediction

Linear regression: Bayesian Ideas and Data Analysis p. 241

Other models

"The BUGS Book"

"Bayesian modeling using WinBUGS"

This article will continuously be updated and I welcome anyone of you to post your experience here.