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On the Approximate Maximum Likelihood Estimation for Diffusion Processes
Asymptotic expansion Asymptotic normality Consistency Dis- crete time observation Maximum likelihood estimation
2016/1/19
The transition density of a diffusion process does not admit an explicit expression in general, which prevents the full maximum likelihood estimation (MLE) based on discretely observed sample paths. A...
Tractable Approximate Robust Geometric Programming
Geometric programming linear programming piecewise-linear function
2015/7/9
The optimal solution of a geometric program (GP) can be sensitive to variations in the problem data. Robust geometric programming can systematically alleviate the sensitivity problem by explicitly inc...
In this paper we describe an approximate dynamic programming policy for a discrete-time dynamical system perturbed by noise. The approximate value function is the pointwise supremum of a family of low...
In this paper we introduce a control policy which we refer to as the iterated approximate value function policy. The generation of this policy requires two stages, the first one carried out off-line, ...
Approximate Dynamic Programming via Iterated Bellman Inequalities
Convex Optimization Dynamic Programming Stochastic Control
2015/7/9
In this paper we introduce new methods for finding functions that lower bound the value function of a stochastic control problem, using an iterated form of the Bellman inequality. Our method is based ...
Quadratic Approximate Dynamic Programming for Input-Affine Systems
approximate dynamic programming stochastic control convex optimization
2015/7/9
We consider the use of quadratic approximate value functions for stochastic control problems with input-affine dynamics and convex stage cost and constraints. Evaluating the approximate dynamic progra...
Statistical modelling of summary values leads to accurate Approximate Bayesian Computations
Statistical modelling summary values leads accurate Approximate Bayesian Computations
2013/6/14
Approximate Bayesian Computations (ABC) are considered to be noisy. We show that ABC can be set up to estimate the mode of the true posterior density exactly, or alternatively provide unbiased estimat...
Approximate Inference for Observation Driven Time Series Models with Intractable Likelihoods
Observation Driven Time Series Models Approximate Bayesian Computation Asymptotic Con-sistency Markov Chain Monte Carlo
2013/4/28
In the following article we consider approximate Bayesian parameter inference for observation driven time series models. Such statistical models appear in a wide variety of applications, including eco...
An Approximate Approach to E-optimal Designs for Weighted Polynomial Regression by Using Tchebycheff Systems and Orthogonal Polynomials
An Approximate Approach E-optimal Designs Weighted Polynomial Regression Using Tchebycheff Systems Orthogonal Polynomials
2013/4/28
In statistics, experimental designs are methods for making efficient experiments. E-optimal designs are the multisets of experimental conditions which minimize the maximum axis of the confidence ellip...
Efficient Estimation of Approximate Factor Models via Regularized Maximum Likelihood
High dimensionality unknown factors principal components sparse matrix conditional sparse thresholding cross-sectional correlation penalized maximum likelihood adaptive lasso heteroskedasticity
2012/11/23
We study the estimation of a high dimensional approximate factor model in the presence of both cross sectional dependence and heteroskedasticity. The classical method of principal components analysis ...
An Interacting Particle Method for Approximate Bayes Computations
Interacting Method Bayes Computations
2012/9/17
Approximate Bayes Computations (ABC) are used for parameter inference when the likelihood function is expensive to evaluate but relatively cheap to sample from. In ABC,a population of particles in the...
Introduction of the so called K-means features caused significant discussion in the deep learning community. Despite their simplicity, these features have achieved state of the art performance on seve...
Approximate Propagation of both Epistemic and Aleatory Uncertainty through Dynamic Systems
Uncertainty Propagation Epistemic Uncertainty Aleatory Uncertainty Dempster-Shafer
2011/7/19
When ignorance due to the lack of knowledge, modeled as epistemic uncertainty using Dempster-Shafer structures on closed intervals, is present in the model parameters, a new uncertainty propagation me...
Approximate Interval Method for Epistemic Uncertainty Propagation using Polynomial Chaos and Evidence Theory
Approximate Interval Method Epistemic Uncertainty Propagation
2011/7/19
The paper builds upon a recent approach to find the approximate bounds of a real function using Polynomial Chaos expansions.
Approximate group context tree: applications to dynamic programming and dynamic choice models
categorical time series group context tree
2011/7/19
The paper considers a variable length Markov chain model associated with a group of stationary processes that share the same context tree but potentially different conditional probabilities.