On accurate and precise generation of generalized Poisson variates

Demirtaş, Hakan

doi:10.1080/03610918.2014.968725

Cited by 10 publications

(2 citation statements)

References 9 publications

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“…There are several techniques to sample from this distribution [30]. We favor the normal approximation sampling technique for its low run-time complexity: With a sufficiently high rate parameter (i.e.…”

Section: ) the Recomputation Policymentioning

confidence: 99%

Optimization of the Model Predictive Control Meta-Parameters Through Reinforcement Learning

Bøhn¹,

Gros²,

Moe³

et al. 2021

Preprint

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Model predictive control (MPC) is increasingly being considered for control of fast systems and embedded applications. However, the MPC has some significant challenges for such systems. Its high computational complexity results in high power consumption from the control algorithm, which could account for a significant share of the energy resources in battery-powered embedded systems. The MPC parameters must be tuned, which is largely a trial-and-error process that affects the control performance, the robustness and the computational complexity of the controller to a high degree. In this paper we propose a novel framework in which any parameter of the control algorithm can be jointly tuned using reinforcement learning (RL), with the goal of simultaneously optimizing the control performance and the power usage of the control algorithm. We propose the novel idea of optimizing the meta-parameters of MPC with RL, i.e. parameters affecting the structure of the MPC problem as opposed to the solution to a given problem. Our control algorithm is based on an event-triggered MPC where we learn when the MPC should be re-computed, and a dual mode MPC and linear state feedback control law applied in between MPC computations. We formulate a novel mixturedistribution policy, and show that with joint optimization we achieve improvements that do not present themselves when optimizing the same parameters in isolation. We demonstrate our framework on the inverted pendulum control task, reducing the total computation time of the control system by 36% while also improving the control performance by 18.4% over the bestperforming MPC baseline.

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Section: ) the Recomputation Policymentioning

confidence: 99%

Optimization of the Model Predictive Control Meta-Parameters Through Reinforcement Learning

Bøhn¹,

Gros²,

Moe³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Hubert Jr et al (2009) test for the value of the GP distribution extra parameter by means of a Bayesian hypotheses test procedure, namely the Full Bayesian Significance Test. Famoye (1997) and Demirtas (2017) provided different methods of sampling from the Generalized Poisson distribution and algorithms for sampling. As regard processes, the GP distribution has been used in different models.…”

Section: A2 Generalized Poisson Distributionmentioning

confidence: 99%

Generalized Poisson Difference Autoregressive Processes

Carallo¹,

Casarin²,

Robert³

2020

Preprint

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This paper introduces a new stochastic process with values in the set Z of integers with sign. The increments of process are Poisson differences and the dynamics has an autoregressive structure. We study the properties of the process and exploit the thinning representation to derive stationarity conditions and the stationary distribution of the process. We provide a Bayesian inference method and an efficient posterior approximation procedure based on Monte Carlo. Numerical illustrations on both simulated and real data show the effectiveness of the proposed inference.

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Generalized Poisson Distributions

Famoye

Lee

2020

Wiley StatsRef: Statistics Reference Online

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The generalized Poisson distribution (GPD) is a two‐parameter discrete distribution. One of the parameters measures the location and the other measures the dispersion. The distribution is unimodal, and it can be skewed to the right or the left. It approaches the normal distribution when the location parameter gets very large. In this article, we present the definition and derive several important properties of the GPD. We review statistical inference and state some characterizations of the distribution. This article is expected to serve as a reference and stimulate further research work on the GPD.

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On accurate and precise generation of generalized Poisson variates

Cited by 10 publications

References 9 publications

Optimization of the Model Predictive Control Meta-Parameters Through Reinforcement Learning

Optimization of the Model Predictive Control Meta-Parameters Through Reinforcement Learning

Generalized Poisson Difference Autoregressive Processes

Generalized Poisson Distributions

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