# 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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### Sample from a distribution and plot in python

I am trying to understand Particle Filter and Importance Sampling Principle from a UniFreiburg Course and this USNA document on particle filters. Simultaneously, I am also trying to write a document ...
17 views

### Particle Filter for navigation through known map

I have some issues with understanding the Particle Filter for navigation through a known map. So, consider a situation where I want to write a Particle filter to navigate through a maze or a map that ...
23 views

### Deriving Hyperparameter updates in Online Interactive Collaborative Filtering

I've been going through "Online Interactive Collaborative Filtering Using Multi-Armed Bandit with Dependent Arms" by Wang et al. and am unable to understand how the update equations for the ...
23 views

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### Bootstrap Particle Filter (Gordon, Salmond, Smith, 2003) - Importance Weights

So, my endeavor to apply the is just for my own edificationI am currently struggling with an attempt to apply a bootstrap particle filter (Gordon, Salmond, Smith, 2003) to a linear, Gaussian state-...
25 views

### extension of sequential probability ratio tests to particle filters?

I've been wondering if there are extensions of the sequential probability ratio test to account for particle filters. I ask because, in my research, I'm working with distributions that cannot be ...
66 views

### Finite grid approximation to the Bayesian filtering problem

I need some hints for solving Ecercise 4.4 from Bayesian Filtering & Smoothing by Simo Särkkä: Select a finite interval in the state space, say, $x \in [-10, 10]$ and discretize it evenly to N ...
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### Predictions after SMC

I have a statistical model given by $$y_t\sim p(y_t|x_t, \theta)\\ x_t\sim p(x_t|x_{t-1},\theta)\\ \theta\sim p(\theta)$$ where $y$ is the only observed component. Using a sequential Monte Carlo ...
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### What are the differences between Bayesian filters and adaptive filters?

I am learning about state estimation and I am having difficulty understanding the difference between Bayesian filters such as Kalman filter and particle filters compared to adaptive filters. According ...
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### Particle filter for diagnosis

I have two annual measurements taken on medical images depicting a lung cancer tumor 's condition. I have likelihood function that taken in the measurement values and estimates malignancy of the tumor....
93 views

### Posterior as prior for correlated parameters [closed]

I want to use the posterior distribution of the model parameters $\theta$ given data in the time frame $[0,t]$ days, $P(\theta|y_{0:t})$; as a prior for the parameters in the time frame $[t+1, t+n]$ ...
151 views

### sequential Monte Carlo sampler, why the extended space and backward kernel?

Hello cross validated, I am currently studying sequential Monte Carlo samplers. My current understanding is as follows: We are interested in the marginal distribution of some sequence of joint ...
83 views

### Kernel for MCMC moves in sequential monte carlo

I'm trying to understand how to employ MCMC moves in a sequential Monte Carlo procedure for estimating static parameters as in the setting described by Chopin. He proposes, for example, the usage of a ...
120 views

### boostrap particle filter marginal likelihood

I want to calculate the marginal likelihood $p(y|\Theta)$ of the parameters of a Markov state space model with unknown parameters $\Theta$ that I am trying to estimate the marginal likelihood (...
247 views

### Why is it necessary to perform resampling step in particle filtering (or sequential monte carlo)?

I read the Wikipedia page on particle filter, it says that during 'prediction-updating', the samples from the distribution are weighted by a likelihood that represents the probability of that particle ...
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### Inference for Maximum Likelihood Estimator Using Particle Filter

How does one compute standard errors for the MLE when using a particle filter approximation to the likelihood? I know that the estimator is asymptotically normal and that the variance-covariance ...
112 views

### Particle filter - expectations

I've recently been implementing some particle filter algorithms and I've realized there is a small detail I might have been doing incorrectly. Unfortunately the descriptions of the algorithms in ...
147 views

### Sequential monte carlo, resampling

In particle filters when one is doing sequential importance sampling, the quantity of interest that is being approximated is usually a weighted sum: \hat x_t = \sum_{i=1}^M \Bigl [f(v^{(i)}_{t}) \...
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### Are the Sequential Monte Carlo algorithm invariant to the step at which we resample?

In a usual textual description (according to SMC in Practice book ) of a SMC algorithm for State-Space models, we usually expand the particles according to the distribution from the transition ...
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### Understanding Sequential Importance Sampling and Particle Filtering

I am struggling with SIS for particle filtering in the following aspect: In particle filtering (as per this book), the objective is to estimate the full posterior $p( x_{0:k} \mid y_{1:k} )$ rather ...
184 views

### notation conditional normal distribution [duplicate]

I'm describing parameter search using a particle filter, for which I use West M. (1993) Approximating Posterior Distributions by Mixture. On page 8 of the document, he states "and $p(\theta)$ is (...
53 views

### Soft Question: What background do I need to understand Feynmann Kac Formulae by Pierre Del Moral?

I am attempting to understand Sequential Monte Carlo(SMC) deeply, but with little theoretical background on probability theory and stochastic processes. Usually, the 'statistics' perspective of markov ...
613 views

### How to calculate importance weights for update step of an SIR (Sequential Importance Resampling) Particle filter?

I understand that one may use a particle filter to solve the filtering problem (estimating the hidden state of a system which can be described as a Hidden Markov Model). If I have a system where I ...
473 views

### Resample-move algorithm for Sequential Monte Carlo

I'm reading about the resample-move strategy, originally by Gilks and Berzuini, but my question will use the slightly more verbose description from the review of Doucet and Johansen, section 4.4, PDF ...
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### Stochastic models: freedom in choosing error type?

I have a general question concerning probabilistic models. Imagine that I have a system with a high-dimensional state $x$ with a pdf $p(x)$. The state $x$ is time-dependent, and a propagating ...
416 views

### 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 ...
259 views

### Bootstrap filter

I am trying to implement bootstrap filter and I'm trying to understand it based on Bootstrap filter/ Particle filter algorithm(Understanding) EDIT: Following is the example that I'm trying to solve: ...
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### Estimating Gamma PDF parameters from data with negative increments

Say we have collected data, and from a physical perspective we know that the collected data should increase positively with time. However the data looks more like this: This data shown in the figure ...
983 views

### What is the best way to apply the log-sum-exp trick in this situation?

I am aware of the "log-sum-exp" trick for calculating the logarithm of sums that handles overflow and underflow issues. However, I would like to know more about how it works. In particular, I am ...
135 views

### weights versus shifted log-weights

I'm having a hard time checking an equality in https://arxiv.org/pdf/1511.01707.pdf. It is unnumbered, immediately before (24), on page 13. Any help would be appreciated. Here is some simplified ...
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### SMC (Particle Filtering) code [closed]

Does anyone know where I can find particle filtering code for R? In particular I'm looking for code for filtering a forward-rate curve.