Stochastic Gradient MCMC with Control Variates
On Connecting Stochastic Gradient MCMC and Differential Privacy
... Among the popular machine learning algorithms, Bayesian inference has realized significant success recently, due to its capacity to leverage expert knowledge and em- ploy uncertainty estimates. Notably, the recently ...
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Stochastic Gradient MCMC for Nonlinear State Space Models
... In this work, we propose particle buffered gradient estimators that generalize the buffered gradient estimators to nonlinear SSMs. In particular, we show how buffering in nonlinear SSMs can be approximated ...
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Communication-Efficient Stochastic Gradient MCMC for Neural Networks
... parallel gradient evalu- ation under communication constraints is distinct from the above related works; in fact, it can be incorporated into their works to improve ...of stochastic gradi- ents in ...
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Stochastic gradient methods for stochastic model predictive control
... sampled gradient to some of the previ- ously computed ones, hence involving direction of descent of more component functions at once yet mantaining the cheap computational effort of SGD [13], [14], [9], [15], ...
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A Class of Control Variates for Pricing Asian Options under Stochastic Volatility Models
... our control variate also works well under the Heston ...of control variates for pricing Asian options under the stochastic volatility ...the stochastic volatility σ t by choosing σ(t) ...
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American options under stochastic volatility: control variates, maturity randomization & multiscale asymptotics
... under stochastic volatility ...efficient control variates for our simulation method using martingales resulting from the approximate price ...
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An Efficient Gradient Projection Method for Stochastic Optimal Control Problems
... The rest of the paper is organized as follows. In Section 2 we set up the stochastic optimal control problem and provide with some assumptions. The gradient projection method is presented in Section ...
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Regression-based Monte Carlo methods with optimal control variates
... Let us mention two relevant modifications of the nested dual algorithm proposed in the literature. Firstly, in Belomestny et al [6] an algorithm not involving sub-simulation was suggested, where an approximation for the ...
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Stochastic Variance-Reduced Policy Gradient
... direct control of the algorithm designer, but it is a function of policy parameters that change over time as the policy is optimized, which is a form of ...policy gradient and ...
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Stochastic gradient Markov chain Monte Carlo
... for MCMC output of each algorithm for the case where d = 10 and N = 10 5 ...The control variate SGMCMC algorithms, SGLD-CV, SG-HMC-CV and SG-NHT-CV are all more efficient than their non-control ...
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Stochastic Gradient Descent as Approximate Bayesian Inference
... the Hessian A if this simple iterate-averaging scheme is to generate good posterior samples. If the condition number is large relative to N , then it may be necessary to replace the scalar step size with a ...
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Stochastic Modified Equations and Dynamics of Stochastic Gradient Algorithms I: Mathematical Foundations
... Observe that this function (in fact, each term in the sum) is monotone-decreasing in µ, and for µ 1 it scales like µ −1 , and for µ 1 it scales like µ −3 . Thus, increasing the momentum parameter decreases the asymptotic ...
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Policy Gradient Methods: Variance Reduction and Stochastic Convergence
... Stochastic rewards may also be considered. Results will continue to hold given appropriate conditions on the randomness, such as conditional independence given the sequence of states, and a bound on the first and ...
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Diffusion approximations and control variates for MCMC
... of control variates to re- duce the variance of additive functionals of Markov Chain Monte Carlo (MCMC) ...Our control variates are defined as linear combinations of functions whose ...
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MCMC Analysis for Optimization of Stochastic Models.
... the MCMC methods described above, for example the Metropolis Hastings algorithm, the convergence of the algorithm requires a proper choice of a proposal ...Adaptive MCMC methods modify the transitions on ...
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Stochastic Approximation and Its Application in MCMC
... 1. We proposed a resampling based Stochastic approximation method for the analysis of large geostatistical data. The main difficulty that lies in the analysis of geostatistical data is the computation time is ...
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Gradient-free MCMC methods for dynamic causal modelling
... using MCMC for Bayesian inference – is determining when the chain has ...most MCMC samplers gives an estimate of the number of sam- ples required to ensure convergence, according to a total variation dis- ...
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Gradient-based MCMC samplers for dynamic causal modelling
... Speci fi cally, we use (a) Hamiltonian MCMC (HMC-E) where sampling is simulated using Hamilton ’ s equa- tion of motion and (b) Langevin Monte Carlo algorithm (LMC-R and LMC-E) that simul[r] ...
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A stochastic algorithm for the valuation of financial derivatives using the hyperbolic distributional variates
... a stochastic approximation method introduced by Okoroafor and Ekere (1999) and have been extremely studied by other authors (see for example Okorafor and Osu, ...
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Forward simulation MCMC with applications to stochastic epidemic models
... an MCMC framework to produce an effective MCMC ...a stochastic process, we are generating data y of how the process ...the stochastic process with a completely new set of random variables Y ...
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