The generalized likelihood ratio(GLR)method is a recently introduced gradient estimation method for handling discontinuities in a wide range of sample performances.We put the GLR methods from previous work into a sing...The generalized likelihood ratio(GLR)method is a recently introduced gradient estimation method for handling discontinuities in a wide range of sample performances.We put the GLR methods from previous work into a single framework,simplify regularity conditions to justify the unbiasedness of GLR,and relax some of those conditions that are difficult to verify in practice.Moreover,we combine GLR with conditional Monte Carlo methods and randomized quasi-Monte Carlo methods to reduce the variance.Numerical experiments show that variance reduction could be significant in various applications.展开更多
基于不需要后验密度解析形式的随机梯度哈密尔顿蒙特卡洛(stochastic gradient Hamiltonian Monte Carlo,SGHMC)方法对AR-GJR-GARCH模型的参数进行了贝叶斯估计。以2019.3.13—2020.1.2和2020.1.3—2020.11.3两个时间段的中证医药指数...基于不需要后验密度解析形式的随机梯度哈密尔顿蒙特卡洛(stochastic gradient Hamiltonian Monte Carlo,SGHMC)方法对AR-GJR-GARCH模型的参数进行了贝叶斯估计。以2019.3.13—2020.1.2和2020.1.3—2020.11.3两个时间段的中证医药指数的数据为例,对本文提出的方法进行了检验。结果显示,所得的参数估计值反映了与该指数的波动性相关的市场背景信息。展开更多
Performing arts and movies have become commercial products with high profit and great market potential. Previous research works have developed comprehensive models to forecast the demand for movies. However,they did n...Performing arts and movies have become commercial products with high profit and great market potential. Previous research works have developed comprehensive models to forecast the demand for movies. However,they did not pay enough attention to the decision support for performing arts which is a special category unlike movies. For performing arts with high-dimensional categorical attributes and limit samples, determining ticket prices in different levels is still a challenge job faced by the producers and distributors. In terms of these difficulties, factorization machine(FM), which can handle huge sparse categorical attributes, is used in this work first. Adaptive stochastic gradient descent(ASGD) and Markov chain Monte Carlo(MCMC) are both explored to estimate the model parameters of FM. FM with ASGD(FM-ASGD) and FM with MCMC(FM-MCMC) both can achieve a better prediction accuracy, compared with a traditional algorithm. In addition, the multi-output model is proposed to determine the price in multiple price levels simultaneously, which avoids the trouble of the models' repeating training. The results also confirm the prediction accuracy of the multi-output model, compared with those from the general single-output model.展开更多
基金the National Natural Science Foundation of China(NSFC)under Grant 72022001,92146003,71901003the Air Force Office of Scientific Research under Grant FA95502010211by Discover GrantRGPIN-2018-05795fromNSERCCanada.
文摘The generalized likelihood ratio(GLR)method is a recently introduced gradient estimation method for handling discontinuities in a wide range of sample performances.We put the GLR methods from previous work into a single framework,simplify regularity conditions to justify the unbiasedness of GLR,and relax some of those conditions that are difficult to verify in practice.Moreover,we combine GLR with conditional Monte Carlo methods and randomized quasi-Monte Carlo methods to reduce the variance.Numerical experiments show that variance reduction could be significant in various applications.
文摘基于不需要后验密度解析形式的随机梯度哈密尔顿蒙特卡洛(stochastic gradient Hamiltonian Monte Carlo,SGHMC)方法对AR-GJR-GARCH模型的参数进行了贝叶斯估计。以2019.3.13—2020.1.2和2020.1.3—2020.11.3两个时间段的中证医药指数的数据为例,对本文提出的方法进行了检验。结果显示,所得的参数估计值反映了与该指数的波动性相关的市场背景信息。
基金the Fund of the Science and Technology Commission of Shanghai Municipality(No.13511506402)
文摘Performing arts and movies have become commercial products with high profit and great market potential. Previous research works have developed comprehensive models to forecast the demand for movies. However,they did not pay enough attention to the decision support for performing arts which is a special category unlike movies. For performing arts with high-dimensional categorical attributes and limit samples, determining ticket prices in different levels is still a challenge job faced by the producers and distributors. In terms of these difficulties, factorization machine(FM), which can handle huge sparse categorical attributes, is used in this work first. Adaptive stochastic gradient descent(ASGD) and Markov chain Monte Carlo(MCMC) are both explored to estimate the model parameters of FM. FM with ASGD(FM-ASGD) and FM with MCMC(FM-MCMC) both can achieve a better prediction accuracy, compared with a traditional algorithm. In addition, the multi-output model is proposed to determine the price in multiple price levels simultaneously, which avoids the trouble of the models' repeating training. The results also confirm the prediction accuracy of the multi-output model, compared with those from the general single-output model.
