Comments (2)
Hi, yes, you're right, all the HMMs considered in the book etc are without control inputs. One quick and dirty way to deal with your issue is to define the
As I often say, if your state-space model is complicated (in some way), and if you just want to implement the bootstrap filter, you might be better off defining directly the Feynman-Kac model that corresponds to the bootstrap filter. So a better way might be to implement directly a FeynmanKac
class, where the sequence of
More details in this notebook tutorial.
BTW, am I assuming correctly that the sequence of
I am going to mark this as "feature request" and keep it open, so that I can think a bit more about it.
Thank you very much for the work you've done creating and maintaining this library 💯
Thanks, I appreciate it!
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Thank you very much for your quick reply. Yes I do know the sequence of
I am very interested in hearing your future thoughts about this study case 🙌
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Related Issues (20)
- Missing feature: Multivariate normal distribution with a different covariance matrix for each particle HOT 2
- Error in guided particle filtering for pre-implemented stochastic volatility model. HOT 4
- Restricting sampling of parameters HOT 4
- Information request regarding particle attribute and predictive steps HOT 1
- nsteps in backward_sampling_mcmc
- Categorical distribution
- Automate upper_bound_log_pt
- Mixture error? HOT 3
- Potential Bug in SMC2 HOT 1
- Forum Creation HOT 3
- Not working given example in the Kalman file HOT 1
- Bug in BayesianVS_gprior HOT 1
- Feature request: parallelisation over the particles? HOT 3
- smc_samplers.AdaptiveTempering may raise an error before reaching exponent == 1. HOT 1
- Problem with missing data HOT 6
- Sensor fusion - multiple `PY` depending on which sensor is returning HOT 6
- How to use this module to implement Particle Metropolis Hastings? HOT 6
- Is there a fast way to define a vector of parameters? HOT 17
- How to get the distribution of x in the lib? HOT 1
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