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Peter Flom
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I have a data, that comes from two sources. Two collections ofsources; the two data sets were obtained from different geographical locationlocations and at different historical timetimes. I do not want to pool them into one data set sample and looselose information about differing values of parameters that generated those collections, but I also cannot use Bayesian hierarchical model, since there would be just two unobservables from which I could not learn anything about hyperparameters. Is there any possible models that could be used in such a problems? I

I know that the question is a bit vague, but I am open to various possibilities.
EDIT
I have a Poissonian countsThe data are counts, distributed as Poisson with some intensities $\lambda _{1}$ and $\lambda _{2}$. Suppose I have two data samples that comes from similar objects and represents number of failures over some period of time. I want to estimate both rates. I dontdon't want to pool them because, for example, the operating conditions (environmental conditions) were different for those two data sources so that intensities were not identical. But I want to be able to borrow some information from each data sample like in hierarchical Bayesian model. But, as I already said, for hierarchical model there is just not enough data sources.

I have a data, that comes from two sources. Two collections of data were obtained from different geographical location and at different historical time. I do not want to pool them into one data sample and loose information about differing values of parameters that generated those collections, but I also cannot use Bayesian hierarchical model, since there would be just two unobservables from which I could not learn anything about hyperparameters. Is there any possible models that could be used in such a problems? I know that the question is a bit vague, but I am open to various possibilities.
EDIT
I have a Poissonian counts data with some intensities $\lambda _{1}$ and $\lambda _{2}$. Suppose I have two data samples that comes from similar objects and represents number of failures over some period of time. I want to estimate both rates. I dont want to pool them because, for example, the operating conditions (environmental conditions) were different for those two data sources so that intensities were not identical. But I want to be able to borrow some information from each data sample like in hierarchical Bayesian model. But, as I already said, for hierarchical model there is just not enough data sources.

I have data from two sources; the two data sets were obtained from different geographical locations and at different historical times. I do not want to pool them into one data set sample and lose information about differing values of parameters that generated those collections, but I also cannot use Bayesian hierarchical model, since there would be just two unobservables from which I could not learn anything about hyperparameters. Is there any possible models that could be used in such a problems?

I know that the question is a bit vague, but I am open to various possibilities.
EDIT
The data are counts, distributed as Poisson with intensities $\lambda _{1}$ and $\lambda _{2}$. Suppose I have two data samples that comes from similar objects and represents number of failures over some period of time. I want to estimate both rates. I don't want to pool them because, for example, the operating conditions (environmental conditions) were different for those two data sources so that intensities were not identical. But I want to be able to borrow some information from each data sample like in hierarchical Bayesian model. But, as I already said, for hierarchical model there is just not enough data sources.

added 640 characters in body
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Tomas
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I have a data, that comes from two sources. Two collections of data were obtained from different geographical location and at different historical time. I do not want to pool them into one data sample and loose information about differing values of parameters that generated those collections, but I also cannot use Bayesian hierarchical model, since there would be just two unobservables from which I could not learn anything about hyperparameters. Is there any possible models that could be used in such a problems? I know that the question is a bit vague, but I am open to various possibilities.
EDIT
I have a Poissonian counts data with some intensities $\lambda _{1}$ and $\lambda _{2}$. Suppose I have two data samples that comes from similar objects and represents number of failures over some period of time. I want to estimate both rates. I dont want to pool them because, for example, the operating conditions (environmental conditions) were different for those two data sources so that intensities were not identical. But I want to be able to borrow some information from each data sample like in hierarchical Bayesian model. But, as I already said, for hierarchical model there is just not enough data sources.

I have a data, that comes from two sources. Two collections of data were obtained from different geographical location and at different historical time. I do not want to pool them into one data sample and loose information about differing values of parameters that generated those collections, but I also cannot use Bayesian hierarchical model, since there would be just two unobservables from which I could not learn anything about hyperparameters. Is there any possible models that could be used in such a problems? I know that the question is a bit vague, but I am open to various possibilities.

I have a data, that comes from two sources. Two collections of data were obtained from different geographical location and at different historical time. I do not want to pool them into one data sample and loose information about differing values of parameters that generated those collections, but I also cannot use Bayesian hierarchical model, since there would be just two unobservables from which I could not learn anything about hyperparameters. Is there any possible models that could be used in such a problems? I know that the question is a bit vague, but I am open to various possibilities.
EDIT
I have a Poissonian counts data with some intensities $\lambda _{1}$ and $\lambda _{2}$. Suppose I have two data samples that comes from similar objects and represents number of failures over some period of time. I want to estimate both rates. I dont want to pool them because, for example, the operating conditions (environmental conditions) were different for those two data sources so that intensities were not identical. But I want to be able to borrow some information from each data sample like in hierarchical Bayesian model. But, as I already said, for hierarchical model there is just not enough data sources.

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Tomas
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Learning from small number of sources

I have a data, that comes from two sources. Two collections of data were obtained from different geographical location and at different historical time. I do not want to pool them into one data sample and loose information about differing values of parameters that generated those collections, but I also cannot use Bayesian hierarchical model, since there would be just two unobservables from which I could not learn anything about hyperparameters. Is there any possible models that could be used in such a problems? I know that the question is a bit vague, but I am open to various possibilities.