# 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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### FIltering noise from data where the noise follows a known distribution but we do not assume a system model

This question is similar to Noise reduction with known noise distribution, but I will try to be more descriptive. Assume we have time-series data $X_t$ and are interpreting it as an equally spaced ...
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### Estimate the direction of the signal using RSSI indicator

I have an antenna that can read RSSI indicator of the signal emitted by the target. The antenna can pan 360. All hooked up to an Arduino. Thus at time t I can read ...
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### Normal Likelihood in Particle Filter

I am following this notebook to implement a particle filter. https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/blob/master/12-Particle-Filters.ipynb What I don't understand is why using ...
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### Can the evidence be the parent of the nodes of Dynamic Bayesian Network?

I am trying to build the structure of my DBN for my system, but I am a bit lost when I compare it to examples of DBN; especially concerning the evidence. At each time $t$ I am getting data from my ...
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### Applying Particle Filter to Dynamic Bayesian Network

I have a DBN where the probabilities are unknown, and then I fill my CPTs randomly. I want to calculate those probabilities over time with the data from my sensors, and according to Artificial ...
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### Slice sampling in Particle Gibbs with Ancestral Sampling

Bear with me as I am not from statistical background. My question is about the implementation of PGAS algorithm as given in Lindsten et. al 2014 concerning sampling in state-space models. The two ...
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### ABC, make tolerance threshold $\epsilon$ adaptive

Briefly the Approximate Bayesian Computation instead of using the exact likelihood function $L(\theta;x)$ tries to approximate this function with the use of the observed summary statistics $s(x_{obs})$...
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### Particle filter with systematically incorrect motion model

I'm familiar with particle filters from robotics, as a tool for synthesizing information about a robot's motor control with measurements to maximize localization accuracy. Given probabilistic models ...
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### Monte Carlo probability approximation vs Histogram

I am trying to learn the sequential Monte Carlo method (particle filter) in data assimilation. In this method, the aim is to approximate the CDF of the target variable having a random sample of the ...
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### Particle Filter for structural credit risk model

Kwon (2012)* proposes a structural credit risk model where the asset value process and the noise are estimated based on the observed equity prices: $S$ - equity prices $V$ - value of the assets $Z$ - ...
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1 vote
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### Modelling Ball Movement using Delayed Measurements with Known Latency

I am a hobby programmer currently developing an algorithm to combine measurements of a dynamically moving ball position (and velocity) from multiple robots. Each robot measure and calculate the ...
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### Simplying Bayes Theorem expression: SIS particle filter posteriori

In the book Beyond the Kalman Filter: Particle Filters for Tracking Applications on page 39 the weight update equation for the particle filter is derived. The derivations begins by introducing the ...
1 vote
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### Calculating the observation density

In the context of link. For a state space model $$x_{k+1} = f(x_{k}, u_k, w_k)$$ $$y_k = H x_k + v_k$$ where the measurement function is assumed linear and Gaussian and the state transition is ...
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### Particle filter: Evaluating Optimal importance density

NOTE I posted this in the math stack exchange but I realized this may be the more appropriate place, old post here. I'm not sure if I should delete one of them so I just linked them in both? I am ...
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