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Aksakal
Aksakal
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I consider Bayesian approach when my data set is not everything that is known about the subject, and want to somehow incorporate that exogenous knowledge into my forecast.

For instance, my client wants a forecast of the loan defaults in their portfolio. They have 100 loans with a few years of quarterly historical data. There were a few occurrences of delinquency (late payment) and just a couple of defaults. If I try to estimate the survival model on this data set, it'll be very little data to estimate and too much uncertainty to forecast.

On the other hand, the portfolio managers are experienced people, some of them may have spent decades managing relationships with borrowers. They have ideas around what the default rates should be like. So, they're capable of coming up with reasonable priors. Note, not the priors which have nice math properties and look intellectually appealing to me. I'll chat with them and extract their experiences and knowledge in the form of those priors.

Now Bayesian framework will provide me with mechanics to marry the exogenous knowledge in the form of priors with the data, and obtain the posterior that is superior to both pure qualitative judgment and pure data driven forecast, in my opinion. This is not a philosophy and I'm not a Bayesian. I'm just using the Bayesian tools to consistently incorporate expert knowledge into the data-driven estimation.

I consider Bayesian approach when my data set is not everything that is known about the subject, and want to somehow incorporate that exogenous knowledge into my forecast.

For instance, my client wants a forecast of the loan defaults in their portfolio. They have 100 loans with a few years of quarterly historical data. There were a few occurrences of delinquency (late payment) and just a couple of defaults. If I try to estimate the survival model on this data set, it'll be very little data to estimate and too much uncertainty to forecast.

On the other hand, the portfolio managers are experienced people, some of them may have spent decades managing relationships with borrowers. They have ideas around what the default rates should be like. So, they're capable of coming up with reasonable priors. Note, not the priors which have nice math properties and look intellectually appealing to me. I'll chat with them and extract their experiences and knowledge in the form of those priors.

Now Bayesian framework will provide me with mechanics to marry the exogenous knowledge in the form of priors with the data, and obtain the posterior that is superior to both pure qualitative judgment and pure data driven forecast, in my opinion. This is not a philosophy and I'm not a Bayesian.

I consider Bayesian approach when my data set is not everything that is known about the subject, and want to somehow incorporate that exogenous knowledge into my forecast.

For instance, my client wants a forecast of the loan defaults in their portfolio. They have 100 loans with a few years of quarterly historical data. There were a few occurrences of delinquency (late payment) and just a couple of defaults. If I try to estimate the survival model on this data set, it'll be very little data to estimate and too much uncertainty to forecast.

On the other hand, the portfolio managers are experienced people, some of them may have spent decades managing relationships with borrowers. They have ideas around what the default rates should be like. So, they're capable of coming up with reasonable priors. Note, not the priors which have nice math properties and look intellectually appealing to me. I'll chat with them and extract their experiences and knowledge in the form of those priors.

Now Bayesian framework will provide me with mechanics to marry the exogenous knowledge in the form of priors with the data, and obtain the posterior that is superior to both pure qualitative judgment and pure data driven forecast, in my opinion. This is not a philosophy and I'm not a Bayesian. I'm just using the Bayesian tools to consistently incorporate expert knowledge into the data-driven estimation.

Source Link
Aksakal
Aksakal
  • 62.3k
  • 6
  • 106
  • 206

I consider Bayesian approach when my data set is not everything that is known about the subject, and want to somehow incorporate that exogenous knowledge into my forecast.

For instance, my client wants a forecast of the loan defaults in their portfolio. They have 100 loans with a few years of quarterly historical data. There were a few occurrences of delinquency (late payment) and just a couple of defaults. If I try to estimate the survival model on this data set, it'll be very little data to estimate and too much uncertainty to forecast.

On the other hand, the portfolio managers are experienced people, some of them may have spent decades managing relationships with borrowers. They have ideas around what the default rates should be like. So, they're capable of coming up with reasonable priors. Note, not the priors which have nice math properties and look intellectually appealing to me. I'll chat with them and extract their experiences and knowledge in the form of those priors.

Now Bayesian framework will provide me with mechanics to marry the exogenous knowledge in the form of priors with the data, and obtain the posterior that is superior to both pure qualitative judgment and pure data driven forecast, in my opinion. This is not a philosophy and I'm not a Bayesian.