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.

96 questions
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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....
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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 $[t+1, t+n]$ ...
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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 ...
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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 ...
42 views

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 (...
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Model selection with this model of a large number of components

I have a discrete time Markov Chain $\{X_n: n \in \mathbb{N}_0\}$ with unknown transition matrix $P \in \mathbb{R}^{M \times M}$ on the state space $\mathcal{S}_X = \{1,2, \dots, M\}$, with $M \geq 2$....
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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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How can I not show the initialization of the estimation in the Extended Kalman Filter?

I'm making estimates through the Extended Kalman Filter and I have a problem related to the vertical axis of my figure, it's too big, so I can not see population dynamics. However, I wish it did not ...
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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 ...
70 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 ...
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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}) \... 1answer 109 views 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 ... 2answers 62 views A doubt on the formula for updating the weights in Sequential Importance Sampling in a State-Space model Let x_{0:t}^{(i)} be the states from time 0 to t from sample i. Similarly for the observations y_{1:t}. The normalized weights are updated according to Where does the term p(y_t|x_t^{(i)})... 0answers 29 views 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 ... 1answer 72 views 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{... 1answer 143 views Importance weight of conditioned particle in conditional SMC In a generic particle filter, I understand the importance weights for each particle are calculated as w_t^s \propto w_{t-1}^s \frac{p(y_t \mid z_t^s) p(z_t^s \mid z_{t-1}^s)}{q(z_t^s \mid z_{t-1}^s, ... 2answers 574 views 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 ... 1answer 94 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 (... 0answers 51 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 ... 1answer 251 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 ... 2answers 340 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 ... 0answers 17 views 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 ... 1answer 278 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, ... 1answer 71 views 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 ... 1answer 156 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: ... 0answers 25 views 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 ... 1answer 602 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 ... 1answer 95 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 ... 2answers 702 views 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. 1answer 167 views Techniques to estimate constant states with particle filter? I have an application where some of my states are constant and therefore have no process noise. Over the course of the estimation process, the uncertainty in these states drops several orders of ... 0answers 74 views Calculate projectile trajectory from 3d points I am trying to calculate the trajectory of a moving object (specifically, a thrown object) through a series of video frames. My tracking algorithm can reliably detect ~90% of the object occurrences ... 0answers 172 views Updating a belief using a particle filter I am using a particle filter to update a belief (the context is the POMCP algorithm found in Silver & Veness, "Monte-Carlo Planning in Large POMDPs"). A belief is represented as a probability mass ... 1answer 62 views How to sample from discrete 3D distribution I am trying to implement a particle filter to track multiple objects. During the propagation phase I need to take N samples from a three dimensional probability distribution, which does not fit known ... 0answers 593 views What is computational complexity of Monte-Carlo sampling? Monte-Carlo method is basically used to integrate multivariate functions. If we use deterministic methods (e.g. Riemann integration), estimation error has an order of O(m^{-\frac{1}{n}}), where m -... 0answers 69 views Sequential Monte Carlo: Joint Smoothing vs Filtering \begin{equation} \begin{split} p(x_{1:t}|y_{1:t})& = \frac{p(y_t|x_{t}) p(x_{t}|x_{t-1}) p(x_{1:t-1}|y_{1:t-1})}{p(y_t|y_{1:t-1})} \end{split} \end{equation} \begin{align} \label{eq:... 1answer 408 views Particle Filter: Confidence Intervals Context This is a basic question about confidence intervals. So the standard way to estimate a confidence interval.Assuming we have a set of N random variables \{X^i\} such that all of them are i.... 1answer 470 views Particle Degeneracy: Variance of the weights This is a qualitative question. In the literature of particle filters/ sequential monte carlo, particle degeneracy is unavoidable. However, frequented cases where it may happen is when the likelihood ... 0answers 159 views Computing Monte Carlo Error: Particle Filters I want to ask a question about the Monte Carlo error of a particle filter. Assume we have information of our of the process of our true states, x_t \forall t and hence, we generate our data y_t. (... 1answer 2k views Hamiltonian Monte Carlo vs. Sequential Monte Carlo I am trying to get a feel for the relative merits and drawbacks, as well as different application domains of these two MCMC schemes. When would you use which and why? When might one fail but the ... 1answer 264 views Empirical Covariance for Set of Particles and Weights In the context of particle filtering. We assume a standard state space model where k is time and i is the particle index.Note: w_k^i are normalised weights. Assume I have a set \{x_k^i, w_k^i\}_{i=... 1answer 491 views Sampling from Likelihood: Likelihood Particle Filter I am particularly baffled with the idea of sampling from the likelihood. To give some context, I am studying particle filters and am investigating the "Likelihood Particle Filter". I am reading this ... 0answers 80 views Discrete Kernel for Sequential Monte Carlo (population monte carlo) I'm attempting understand, and use, the population Monte Carlo algorithm found here https://arxiv.org/abs/0805.2256 for approximate Bayesian computation. However I think this is a general SMC question,... 1answer 116 views Exact Particle Filtering Derivation I am very confused with one of the derivations used by this paper http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.409.2389&rep=rep1&type=pdf in pg 1021 of this paper. It is a simple ... 1answer 158 views Application of the law of large numbers In the book by Christian Robert and George Casella on Monte Carlo Statistical Methods, they use an argument of LLN on pages 551 and 552. I'm attaching the argument in this screen shot t is ... 0answers 173 views KL divergence in Sequential Monte Carlo Suppose at step t the particle approximation of SMC in d dimensions is given by \sum_{k=1}^N w_k\delta(\vec{x}-\vec{x}_k), and at the subsequent step, t+1 (after using Bayes' law to update ... 1answer 33 views Particle Filtering with Nonlinear Observation Equation of 2 set of variables I am stuck with this problem in my research. I am having a State Space Model like the below mentioned one: State Equation: \mathbf{d}_k = \mathbf{d}_{k-1} + \mathbf{u}_k + \boldsymbol{\epsilon}_k ... 2answers 156 views throwing away all Gibbs samples after approximation This is more of a theory question, consider:P(w_1|D)=\int P(w_1|S)P(S|D)d(S)$$which we approximate via Gibbs sampling S (assume the initial state of the Gibbs sampler is denoted by M_0), ... 1answer 42 views Doucet Notation- d in expectation estimate In the book Sequential Monte Carlo Methods in Practice, the beginning of section 1.3.1. I am confused by the notation used$$P_N(dx_{0:t}|y_{0:t})=\frac{1}{N}\sum_{i=1}^{N}{\delta_{x_{0:t}^{(i)}}(...
Whenever the particle filter is derived (I used a different condition for $u_t$ as a solution to the nonlinear filtering problem; \$x_{t+1} \mid x_t \sim f_{\theta}(x_{t+1} \mid x_t,u_t) \\ y_{t} \...