Body: <p>I am looking at a paper that references dynamic bayesian networks--the simplest case being a hidden markov model. The author uses a particle filter to model the posterior distribution for the current state, given the sensor observations.</p>
  <p>So I understand what a particle filter is and the particle filtering algorithm. I was wondering why people seem to use particle filters for these dynamic models, instead of something like variational inference? I imagine variational inference has more flexibility when the network gets more complicated--such as combining discrete and continuous nodes--or when the network gets very large and hence computing particles may take time.</p>
  <p>So the main question is why do most folks use particle filters versus variational inference for dynamic models? Is there some obvious reason I am missing or something?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2024-09-24T20:32:14.253Z
Comments.Text: one restricts the posterior to a class of function, the other one not necessarily
Comments.UserDisplayName: Alberto
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2024-09-24T20:44:16.060Z
Comments.Text: @Alberto so if that restriction is the only issue, then I imagine I could use either particle filtering or variational inference for dynamic models. I would probably benchmark against training data, but otherwise either approach would be fine--if I am interpreting your comment correctly.
Comments.UserDisplayName: krishnab
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2024-09-25T16:37:33.073Z
Comments.Text: imho depends highly from what you want to estimate, the dimensionality of the problem but more importantly, the computational budget
Comments.UserDisplayName: Alberto
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2024-09-25T22:28:53.907Z
Comments.Text: Is computing the particle filter faster than variational inference? I figured computing all of the particles and updating them would be computationally expensive to do. Also there is a need to generate new particles periodically. But I guess if the ELBO is very non-convex, then that could take some time too. But is the usual appeal that particle filters are faster than computing variational inference? Is it simple like that or are there other considerations.
Comments.UserDisplayName: krishnab
Tags: <bayesian-networks><hidden-markov-model><variational-inference><probabilistic-graphical-models>
Title: Particle filtering versus variational inference in dynamic bayesian networks
UserDisplayName: krishnab
ThreadText: # Particle filtering versus variational inference in dynamic bayesian networks
  
  (Asked by: username_0 on 2024-09-24)
  
  I am looking at a paper that references dynamic bayesian networks--the simplest case being a hidden markov model. The author uses a particle filter to model the posterior distribution for the current state, given the sensor observations.
  
  So I understand what a particle filter is and the particle filtering algorithm. I was wondering why people seem to use particle filters for these dynamic models, instead of something like variational inference? I imagine variational inference has more flexibility when the network gets more complicated--such as combining discrete and continuous nodes--or when the network gets very large and hence computing particles may take time.
  
  So the main question is why do most folks use particle filters versus variational inference for dynamic models? Is there some obvious reason I am missing or something?
  
  **Tags:** bayesian-networks, hidden-markov-model, variational-inference, probabilistic-graphical-models
  > **Comments:**
  > **username_1**: one restricts the posterior to a class of function, the other one not necessarily  
  > **username_0**: @Alberto so if that restriction is the only issue, then I imagine I could use either particle filtering or variational inference for dynamic models. I would probably benchmark against training data, but otherwise either approach would be fine--if I am interpreting your comment correctly.  
  > **username_1**: imho depends highly from what you want to estimate, the dimensionality of the problem but more importantly, the computational budget  
  > **username_0**: Is computing the particle filter faster than variational inference? I figured computing all of the particles and updating them would be computationally expensive to do. Also there is a need to generate new particles periodically. But I guess if the ELBO is very non-convex, then that could take some time too. But is the usual appeal that particle filters are faster than computing variational inference? Is it simple like that or are there other considerations.  
  
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  ## 0 answers
  
  