Body: <p>I am not an expert in machine learning. Recently, I want to construct a data-driven model based the neural network. The problem is that I want from my algorithm to learn an output in the form of a probability distribution (maybe to learn the parameters of a predefined probability distribution). This is because the phenomenon I am trying to model is highly uncertain.
  In my case, I am dealing with a supervised learning. Hence, the output which is supposedly in form of a probability distribution should be compared with data points to assess the performance. Could you please suggest to me a reference dealing with this issue.
  Thank you so much for your valuable help.</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2022-02-28T09:39:44.023Z
Comments.Text: Can you be more specific about the distribution that you want to learn? Do you want to learn any distribution? Do you know that regular neural networks for classification produce a probability vector? I don't think that's what you want though.
Comments.UserDisplayName: nbro
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2022-03-01T10:26:24.010Z
Comments.Text: Thanks for your response. I know that stochasticity is an intrinsic property of my system so I need to model it explicitly (regression rather than classification). I want my system to learn for example the parameters of a normal distribution. Regular neural networks  produce a probability vector but I think perhaps this is not a property of the 'law' govening the data exactly.
Comments.UserDisplayName: marouane bouadi
Tags: <neural-networks><probability-distribution><performance>
Title: Neural network for an output in the form of a probability distribution
UserDisplayName: marouane bouadi
ThreadText: # Neural network for an output in the form of a probability distribution
  
  (Asked by: username_0 on 2022-02-24)
  
  I am not an expert in machine learning. Recently, I want to construct a data-driven model based the neural network. The problem is that I want from my algorithm to learn an output in the form of a probability distribution (maybe to learn the parameters of a predefined probability distribution). This is because the phenomenon I am trying to model is highly uncertain.
  In my case, I am dealing with a supervised learning. Hence, the output which is supposedly in form of a probability distribution should be compared with data points to assess the performance. Could you please suggest to me a reference dealing with this issue.
  Thank you so much for your valuable help.
  
  **Tags:** neural-networks, probability-distribution, performance
  
  > **Comments:**
  > **username_1**: Can you be more specific about the distribution that you want to learn? Do you want to learn any distribution? Do you know that regular neural networks for classification produce a probability vector? I don't think that's what you want though.  
  > **username_0**: Thanks for your response. I know that stochasticity is an intrinsic property of my system so I need to model it explicitly (regression rather than classification). I want my system to learn for example the parameters of a normal distribution. Regular neural networks  produce a probability vector but I think perhaps this is not a property of the 'law' govening the data exactly.  
  
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  ## 0 answers
  
  