This paper demonstrates that the logarithm of the variate itself can be used in the calculation of differential entropy among random variables known to be multiples and powers of a common underlying variate. It also presents a function that gives the entropy of these related variates, using only statistics of the logarithm of the variate plus a constant, regardless of the nature or the paramete...

We introduce a new Markov chain Monte Carlo (MCMC) sampler called the Markov Interacting Importance Sampler (MIIS). The MIIS sampler uses conditional importance sampling (IS) approximations to jointly sample the current state of the Markov Chain and estimate conditional expectations, possibly by incorporating a full range of variance reduction techniques. We compute Rao-Blackwellized estimates ...

Cement-based piezoelectric materials are widely used due to the fact that compared with common smart materials, they overcome the defects of structure-incompatibility and frequency inconsistency with a concrete structure. However, the present understanding of the mechanical behavior of cement-based piezoelectric smart materials under impact load is still limited. The dynamic characteristics und...

The waste-recycling Monte Carlo (WR) algorithm introduced by physicists is a modification of the (multi-proposal) Metropolis-Hastings algorithm, which makes use of all the proposals in the empirical mean, whereas the standard (multi-proposal) MetropolisHastings algorithm only uses the accepted proposals. In this paper, we extend the WR algorithm into a general control variate technique and exhi...

Agent evaluation in stochastic domains can be difficult. The commonplace approach of Monte Carlo evaluation can involve a prohibitive number of simulations when the variance of the outcome is high. In such domains, variance reduction techniques are necessary, but these techniques require careful encoding of domain knowledge. This paper introduces baseline as a simple approach to creating low va...

Stochastic gradient optimization is a class of widely used algorithms for training machine learning models. To optimize an objective, it uses the noisy gradient computed from the random data samples instead of the true gradient computed from the entire dataset. However, when the variance of the noisy gradient is large, the algorithm might spend much time bouncing around, leading to slower conve...

Adaptive sensor management (scheduling) is usually formulated as a finite horizon POMDP and implemented using sequential Monte Carlo. In Monte Carlo, variance reduction is important for the reliable performance of the sensor scheduler. In this paper, we propose a Control Variate method for variance reduction when the sensor is scheduled using the Kullbach Leibler criterion.