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gung - Reinstate Monica
gung - Reinstate Monica
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I was wondering how the Bayesians in the CrossValidated community view the problem of model uncertainty and how they prefer to deal with it? I will try to pose my question in two parts:

  1. How important (in your experience  /opinion opinion) is dealing with model uncertainty? I haven't found any papers dealing with this issue in the machine learning community, so I'm just wondering why.

  2. What are the common approaches for handling model uncertainty (bonus points if you provide references)? I've heard of Bayesian model averaging, though I am not familiar with the specific techniques  /limitations limitations of this approach. What are some others and why do you prefer one over another?

I was wondering how the Bayesians in the CrossValidated community view the problem of model uncertainty and how they prefer to deal it? I will try to pose my question in two parts:

  1. How important (in your experience/opinion) is dealing with model uncertainty? I haven't found any papers dealing with this issue in the machine learning community, so I'm just wondering why.

  2. What are the common approaches for handling model uncertainty (bonus points if you provide references)? I've heard of Bayesian model averaging, though I am not familiar with the specific techniques/limitations of this approach. What are some others and why do you prefer one over another?

I was wondering how the Bayesians in the CrossValidated community view the problem of model uncertainty and how they prefer to deal with it? I will try to pose my question in two parts:

  1. How important (in your experience  / opinion) is dealing with model uncertainty? I haven't found any papers dealing with this issue in the machine learning community, so I'm just wondering why.

  2. What are the common approaches for handling model uncertainty (bonus points if you provide references)? I've heard of Bayesian model averaging, though I am not familiar with the specific techniques  / limitations of this approach. What are some others and why do you prefer one over another?

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Nick
Nick
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Addressing model uncertainty

I was wondering how the Bayesians in the CrossValidated community view the problem of model uncertainty and how they prefer to deal it? I will try to pose my question in two parts:

  1. How important (in your experience/opinion) is dealing with model uncertainty? I haven't found any papers dealing with this issue in the machine learning community, so I'm just wondering why.

  2. What are the common approaches for handling model uncertainty (bonus points if you provide references)? I've heard of Bayesian model averaging, though I am not familiar with the specific techniques/limitations of this approach. What are some others and why do you prefer one over another?