<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>

CC BY-SA 4.0
2022-02-28T09:39:44.023Z
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.
nbro
CC BY-SA 4.0
2022-03-01T10:26:24.010Z
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.
marouane bouadi
<neural-networks><probability-distribution><performance>
Neural network for an output in the form of a probability distribution
marouane bouadi
# 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

