# Questions tagged [particle-filter]

Particle filters (or sequential Monte Carlo) is a form of genetic simulation algorithm used for filtering problems in signal analysis and time series analysis.

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### How does Particle Filters work?

I'm trying to figure out how particle filter works. Assume that I have selected propability function called $a \sim Gauss(\mu, \sigma)$. We call it proposial (Gaussian) Distribution. Then we have ...
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### Do I need to know the distribution of the noise before I'm using Monte Carlo Sampling?

I'm going to use Particle Filter, which is a Monte Carlo Sampling. My simple question is: Do I need to know the distribution of the noise before I'm using Monte Carlo Sampling? Or can I just use a ...
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### How to handle additional measurement uncertainty from non-fixed sensor with Kalman filter?

Most examples I've seen of Kalman filters assume a measurement w.r.t. a global reference frame; i.e. direct measurement of location, and therefore direct measurement of a state. Consider the scenario ...
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### Seeking more information on this Bayesian inference method

I came across an algorithm for performing Bayesian inference, see section 3.2 of this paper. Their approach is outlined as (where the "belief state" is a distribution on a discrete set of ...
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### How do particle filters diversify the population?

I am trying to understand how to compute Bayesian filter with particle filter algorithms. My background is more from MCMC, importance sampling, nested sampling. I find the notations a bit confusing. ...
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### Particle Gibbs Sampler For Regime-Switching Nonlinear Gaussian SSM

I'm reading this paper on using a non-linear Gaussian SSM for measuring regime-switching leverage effect using stock market data. I'm using it as jump-off point for an undergraduate paper. My advisor ...
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### Particle filter/SMC - dynamic rotation in ICA (independent component analysis)

I struggle with the applicability of the bootstrap particle filter within dynamic rotations in independent component analysis. To be clear, suppose the following: $$Y_{t} = R(\delta_{t})\epsilon_{t}$$ ...
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### Bayes filter with delayed measurements

I have some straight and curve pieces with numbers, they are used to build tracks (of $5$ lanes) for my cars (figure $1$), I can send commands to the cars using an SDK on the Raspberry (set the speed ...
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### Particle filter maximum likelihood with a discrete (Bernoulli) state variable, non-smooth loglikelihood

My model looks like this \begin{align} \begin{split} dY_{t} & = \sigma_{t} dW_{t} + Z_t dN_t \\ d\lambda_t & = \alpha(\lambda_\infty - \lambda_t)dt + \beta dN_t \end{split} \end{...
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### Stochastic volatility: particle filter vs Metropolis-Hastings

In many of the papers on particle filter I've read (e.g. Douc, Moulines and Olsson, 2007), stochastic volatility is a common example to show that a newly-proposed filter is working. At the same time, ...
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### Multi-Target Tracking Filters

I am trying to solve a multi-target tracking problem, which is in some parts different to some filters I have already researched such as the PHD filter. I am asking for advise which filters to start ...