Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The decentralized particle lter (DPF) was proposed recently to increase the level of parallelism of particle ltering. Given a decomposition of the state space into two nested sets of variables, the DPF uses a particle lter to sample the rst set and then conditions on this sample to generate a set of samples for the second set of variables. The DPF can be understood as a variant of the popular Rao-Blackllized particle lter (RBPF), where the second step is carried out using Monte Carlo approximations instead of analytical inference. As a result, the range of applications of the DPF is broader than the one for the RBPF. ,  improve the DPF in two ways. First,  derive a Monte Carlo approximation of the optimal proposal distribution and, consequently, design and implement a more ecient look-ahead DPF. Although the decentralized lters re initially designed to capitalize on parallel implementation,  show that the look-ahead DPF can outperform the standard particle lter even on a single machine. Second,  propose the use of bandit algorithms to automatically congure the state space decomposition of the DPF.
['Mohamed Osama Ahmed', 'Pouyan Taghipour Bibalan', 'Nando de Freitas', 'Simon Fauvel']
I am writing a research paper on a idea Decentralized, Adaptive, Look-Ahead Particle Filtering
[{"from": "human", "value": "I am writing a research paper on a idea Decentralized, Adaptive, Look-Ahead Particle Filtering"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The decentralized particle lter (DPF) was proposed recently to increase the level of parallelism of particle ltering. Given a decomposition of the state space into two nested sets of variables, the DPF uses a particle lter to sample the rst set and then conditions on this sample to generate a set of samples for the second set of variables. The DPF can be understood as a variant of the popular Rao-Blackllized particle lter (RBPF), where the second step is carried out using Monte Carlo approximations instead of analytical inference. As a result, the range of applications of the DPF is broader than the one for the RBPF. ,  improve the DPF in two ways. First,  derive a Monte Carlo approximation of the optimal proposal distribution and, consequently, design and implement a more ecient look-ahead DPF. Although the decentralized lters re initially designed to capitalize on parallel implementation,  show that the look-ahead DPF can outperform the standard particle lter even on a single machine. Second,  propose the use of bandit algorithms to automatically congure the state space decomposition of the DPF."}]