Answers.Body: <p>My understanding is that the answer to this question is basically 'ancestral sampling is used in autoregressive models because it fits well the structure/dynamics of autoregressive models (the ancestor-descendent relationship, etc.)'. It's not a very satisfying answer, but my understanding is that it's correct.</p>
  <p>If anyone has a better answer, feel free to post.</p>
  
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Answers.CreationDate: 2021-06-25T07:01:51.110Z
Answers.LastActivityDate: 2021-06-25T07:01:51.110Z
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Body: <p>I have been reading about <a href="https://deepai.org/machine-learning-glossary-and-terms/autoregressive-model" rel="nofollow noreferrer">autoregressive models</a>. Based on what I've read, it seems to me that all autoregressive models use <a href="https://www.reddit.com/r/deeplearning/comments/cgqpde/what_is_ancestral_sampling/" rel="nofollow noreferrer">ancestral sampling</a>. For instance, <a href="http://proceedings.mlr.press/v32/gregor14.pdf" rel="nofollow noreferrer">this</a> paper says the following in <strong>Abstract</strong>:</p>
  <blockquote>
  <p>We introduce a deep, generative autoencoder capable of learning hierarchies of distributed representations from data. Successive deep stochastic hidden layers are equipped with autoregressive connections, which enable the model to be sampled from quickly and exactly via ancestral sampling.</p>
  </blockquote>
  <p>However, what I don't understand is why (as I understand it) all autoregressive models use ancestral sampling. Why is ancestral sampling used in autoregressive models?</p>
  
Tags: <machine-learning><sampling>
Title: Why is ancestral sampling used in autoregressive models?
UserDisplayName: The Pointer
ThreadText: # Why is ancestral sampling used in autoregressive models?
  
  (Asked by: username_0 on 2021-06-24)
  
  I have been reading about [autoregressive models](https://deepai.org/machine-learning-glossary-and-terms/autoregressive-model). Based on what I've read, it seems to me that all autoregressive models use [ancestral sampling](https://www.reddit.com/r/deeplearning/comments/cgqpde/what_is_ancestral_sampling/). For instance, [this](http://proceedings.mlr.press/v32/gregor14.pdf) paper says the following in **Abstract**:
  
  > We introduce a deep, generative autoencoder capable of learning hierarchies of distributed representations from data. Successive deep stochastic hidden layers are equipped with autoregressive connections, which enable the model to be sampled from quickly and exactly via ancestral sampling.
  
  However, what I don't understand is why (as I understand it) all autoregressive models use ancestral sampling. Why is ancestral sampling used in autoregressive models?
  
  **Tags:** machine-learning, sampling
  
  
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  ## 1 answers
  
  ### Answer by username_0 on 2021-06-25. Score: 0 | Accepted answer
  
  My understanding is that the answer to this question is basically 'ancestral sampling is used in autoregressive models because it fits well the structure/dynamics of autoregressive models (the ancestor-descendent relationship, etc.)'. It's not a very satisfying answer, but my understanding is that it's correct.
  
  If anyone has a better answer, feel free to post.
  
  
  ---
  
  