Dear Doctor Oscar,
 
Sorry for not noticing that you are the author of the RJaCGH package.

But I noticed that hidden Markov model in your package is with non-homogeneous transition probabilities. Here in my work, the HMM is just a first-order homogeneous Markov chain, i.e. the  transition  matrix is constant. 
 
So, Could you please tell me how can I adjust the R functions in your package to implement my analysis?
 
Best Regards,
 
James Allan


--- 12年2月27日，周一, Oscar Rueda [via R] <ml-node+s789695n4424152h18@n4.nabble.com> 写道：


发件人: Oscar Rueda [via R] <ml-node+s789695n4424152h18@n4.nabble.com>
主题: Re: Bayesian Hidden Markov Models
收件人: "monkeylan" <lanjinchi@yahoo.com.cn>
日期: 2012年2月27日,周一,下午6:05


Dear James, 
Although designed for the analysis of copy number CGH microarrays, RJaCGH 
uses a Bayesian HMM model. 

Cheers, 
Oscar 


On 27/2/12 08:32, "monkeylan" <[hidden email]> wrote: 


> Dear R buddies, 
> 
> Recently, I attempt to model the US/RMB Exchange rate log-return time series 
> with a *Hidden Markov model (first order Markov Chain & mixed Normal 
> distributions). * 
> 
> I have applied the RHmm package to accomplish this task, but the results are 
> not so satisfying. 
> So, I would like to try a *Bayesian method *for the parameter estimation of 
> the Hidden Markov model. 
> 
> Could anyone kindly tell me which R package can perform Bayesian estimation 
> of the model? 
> 
> Many thanks for your help and time. 
> 
> Best Regards, 
> James Allan 
> 
> 
> -- 
> View this message in context: 
> http://r.789695.n4.nabble.com/Bayesian-Hidden-Markov-Models-tp4423946p4423946. 
> html 
> Sent from the R help mailing list archive at Nabble.com. 
> 
> ______________________________________________ 
> [hidden email] mailing list 
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code. 
Oscar M. Rueda, PhD. 
Postdoctoral Research Fellow, Breast Cancer Functional Genomics. 
Cancer Research UK Cambridge Research Institute. 
Li Ka Shing Centre, Robinson Way. 
Cambridge CB2 0RE 
England 




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