Talk:Particle filter

message: Seems to have no mention at all of particle filters... I'm going to remove it
$ynoptik_m4yh3m (talk) 02:58, 17 May 2016 (UTC)
thread_title:  RADAR example 

message: SIR stands for "sequential importance resampling", not "sampling importance resampling". 
See any of Doucet's work:
http://people.cs.ubc.ca/~arnaud/doucet_johansen_tutorialPF.pdf
http://people.cs.ubc.ca/~arnaud/samsi_course.html
129.31.206.59 (talk) 04:26, 11 August 2010 (UTC)
thread_title:  Corrected meaning of SIR 

message: Quoting: "They are something like an Extended Kalman filter (EKF)"
They are NOTHING like an EKF! 
* The EKF:
# uses a 1st order linearisation around the current estimate.
# assumes that the process and measurement noise of the system are Gaussian.
* The particle filter:
# uses the actual nonlinear dynamics for propagating the system.
# it can deal with extreme non-Gaussian and multimodal noise distributions.
# since it is a Monte Carlo based technique, it can incorporate easily and accurately in its structure any non-standard information (like hard/soft constrains, a-priori knowledge), improving thus its performance.
and many more...
(if I will have time I might add some new things in the article)
: Have changed the offending phrase, hope it's an improvement.
: As to why it's a valid comparison in the first place, 
: * It's in an encyclopedia article.  That means it must be useful to non-specialists.
: * The EKF does solve a related problem and it's probably the best known filtering algorithm after the Kalman filter itself, and the best known one for nonlinear state-space models (if there's a better known one put that in instead).  : * It's in an introductory paragraph, and an appropriate place for an informal introduction.  
: Of course there are major differences compared with the EKF, but it's still a worthwhile comparison for anyone new to particle filters.  
:: I'm with the original author on this one.  It's pedantic at best to say that particle filtering is "nothing" like EKF.  Both exist to solve nonlinear estimation problems.  Particle filtering, of course, goes about this in a very different way in trying to approximate important samples of the density rather than forcing a Gaussian estimate via linearization.  But the general purpose and scope of the two approaches is quite similar. Mateoee 20:42, 4 November 2006 (UTC)
thread_title:  Nice work but... 

message: The definition could be clearer.  For example, what is \beta \mid \beta_k in the definition?  It means conditional probability of \beta, so why not say P(\beta \mid \beta_k)?
:Not necessarily.  If you're talking about:
::\beta_k|\beta_{k-1} \sim p_{\beta_k|\beta_{k-1}}(\beta|\beta_{k-1})
:then that means \beta_k|\beta_{k-1} is a distribution, not a probability. Cburnett July 8, 2005 16:05 (UTC)
::: So this notation convention should be explained, since it is non standard enough for some of us to understand it, no? --Powo 10:51, 15 January 2007 (UTC)
replies: text: Indeed the notation can use some improvement and few words of explanation should be added here and there. \beta_k|\beta_{k-1} \sim p_{\beta_k|\beta_{k-1}}(\beta|\beta_{k-1}) wants to say "if \beta_{k-1} is the state at the time k-1 then the probability density of \beta_k, the state at the time k, is p_{\beta_k|\beta_{k-1}}(\beta)." Here p_{\beta_k|\beta_{k-1}} is a function and \beta its argument. Putting the conditional symbol | also in the argument makes little sense. Later on the article drops the subscript of the density function completely. It should stick to one or the other and define the notation properly or link to an article that does. Unfortunately such terse expression and  notation (perhaps misuse of notation?) are common in the PF literature and make understanding hard for the novice.  Jmath666 02:36, 11 March 2007 (UTC)
thread_title:  missing probability symbol 

message: I find this article too hard to understand right now. Examples could help. Thanks, --Abdull 14:52, 28 February 2006 (UTC)
replies: text: Indeed. Perhaps something like my explanation above would be useful, too. Jmath666 03:09, 11 March 2007 (UTC)
thread_title:  Eh? 

message: I agree with Matt - see section Eh?: it would be great to have a simple example. The first 2 paragraphs read well, but then it gets technical without a leading example or illustration. I read about particle filtering in computer vision books and wanted to broaden my horizon on this topic, but it helped limitedly. I'm just not the right person to help out here - I understand too little. Hope someone else will find the time. Regards from Bucuresti, Romania. Rasche (talk) 17:01, 22 August 2013 (UTC)
thread_title:  Simple example please (Eh? no. 2) 

message: I removed this article from the computer vision category.  The P-filter is probably usful in some part of CV but
# It is not a concept developed within CV or specific to CV.
# There is no material in this article which relates it to CV.
--KYN 15:09, 28 July 2007 (UTC)
thread_title:  Computer vision category 

message: In the following line:
5) Generate another uniform u from [0, m_k]
Maybe I missed something, but m_k has not been specified.
Uliba 11:20, 31 October 2007 (UTC)
thread_title:  Direct version: missing notation 

message: Although the article mentions an article by Kitagawa, it gives no actual citation. Either supply the citation or remove the comment. Preferably the former. Bill Jefferys 02:26, 15 November 2007 (UTC)
replies: text: "Monte Carlo Filter and Smoother for Non-Gaussian Nonlinear State Space Models", Genshiro Kitagawa Journal of Computational and Graphical Statistics, Vol. 5, No. 1 (Mar., 1996), pp. 1-25 
Would the person who added the comment about Kitagawa in the main article please state if this is the right citation? Bill Jefferys 22:10, 15 November 2007 (UTC)
thread_title:  Kitagawa (1996) Cite Needed 

message: I've never seen the superscript-in-parentheses before.  What does it mean?  I'm guessing it's not exponentiation... Leptogenesis (talk) 06:22, 16 February 2009 (UTC)
thread_title:  Notation? 

message: There is no explanation of how the plot was generated or even what variable is being estimated.  Maybe it's the beta coefficient of a stock?  Whatever it is needs to be clearly stated as do the observation and propagation models (pdfs).  However, even with this additional information the plot is misleading since it shows the mean of the estimated variable which obfuscates one of the main advantages of the particle filter: that it is non-parametric.  It would be an improvement to plot the ML estimate of the variable instead of the mean, but it would probably be even more informative to plot the particle ancestry of the particles alive at the final time step.  This would help illustrate the multi-hypothesis nature of the particle filter. Mark 20:53, 15 June 2009 (UTC)
thread_title:  Uninformative and Misleading Figure 

message: In the paragraph ...
All Bayesian estimates of xk follow from the posterior distribution p(xk | y0,y1,…,yk). In contrast, the MCMC or importance sampling approach would model the full posterior p(x0,x1,…,xk | y0,y1,…,yk).
I think the correct term for p(xk | y0,y1,…,yk) is just the conditional distribution, as what's called the "full posterior" is usually just called the posterior.
Also, since this is the "nowcast" distribution, it's also called the filter distribution, which I believe is used in other parts of the article.
Anyone reading this?  I'm going to change the article to reflect reality if not.
Bradweir (talk) 04:28, 12 July 2011 (UTC)
thread_title:  Conditional/Filter vs. Posterior 

message: ...is never explained.  —Preceding unsigned comment added by IskaralPust (talk • contribs) 08:20, 12 May 2011 (UTC)
thread_title:  Proposal Distribution 

message: The current version of the "Model" section claims that particle filters assume the system state is 1st order Markov and that the observations depend only on the current state.  I realize that most introductory descriptions of particle filters assume this for simplicity, but I didn't think that particle filters necessarily make that assumption.  ??  — Preceding unsigned comment added by 99.113.169.222 (talk) 07:07, 4 June 2012 (UTC)
thread_title:  Is the "Model" Section Incorrect? 

message: As I understand this statement, it is not true at all. The samples drawn by an SMC algorithm are not restricted to be discrete (i.e. defined in a grid-division of a continuous space). In other words, in principle the samples (or particles in a particle filtering context) can be anywhere within the support of the target distribution.  Comment added by Iglesiasg 9:58, 1 April 2014 (UTC)
thread_title:  SMC methods use a grid-based approach? 

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thread_title:  External links modified 

message: First sentence of the 3 paragraphs in the first section has a typo. Not an expert in this subject so not sure how to update properly.
Particle filters implement the prediction-updating updates in an approximate manner. 
50.207.97.110 (talk) 21:27, 12 November 2019 (UTC)
replies: text: I think I solved this.--Mvqr (talk) 11:38, 16 February 2020 (UTC)
thread_title:  Typo 

message: As Jeremy Kun has pointed out here, there is a style of writing in statistics that uses superfluous jargon, is verbose, and is altogether far more complicated than it needs to be. This article in its current state can serve as a prime example. --Svennik (talk) 11:45, 29 May 2020 (UTC)
replies: text: You are correct, however 60-70% of journal articles are all choke full of jargon. It is sometimes hard to escape.--Mvqr (talk) 10:03, 30 May 2020 (UTC)
thread_title:  Superfluous jargon and obfuscation