Stochastic Approximation in Monte Carlo Computation
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Trajectory Averaging for Stochastic Approximation MCMC Algorithms
The Annals of Statistics 2010, Vol. 38, No. 5, 2823–2856 DOI: 10.1214/10-AOS807 c Institute of Mathematical Statistics, 2010 TRAJECTORY AVERAGING FOR STOCHASTIC APPROXIMATION MCMC ALGORITHMS1 By Faming Liang Texas A&M University The subject of stochastic approximation was founded by Rob- bins and Monro [Ann. Math. Statist. 22 (1951) 400–407]. After five decades of continual development, it has developed into an important area in systems control and optimization, and it has also served as a prototype for the development of adaptive algorithms for on-line esti- mation and control of stochastic systems. Recently, it has been used in statistics with Markov chain Monte Carlo for solving maximum likelihood estimation problems and for general simulation and opti- mizations. In this paper, we first show that the trajectory averaging estimator is asymptotically efficient for the stochastic approxima- tion MCMC (SAMCMC) algorithm under mild conditions, and then apply this result to the stochastic approximation Monte Carlo algo- rithm [Liang, Liu and Carroll J. Amer. Statist. Assoc. 102 (2007) 305–320]. The application of the trajectory averaging estimator to other stochastic approximation MCMC algorithms, for example, a stochastic approximation MLE algorithm for missing data problems, is also considered in the paper. 1. Introduction. Robbins and Monro (1951) introduced the stochastic approximation algorithm to solve the integration equation (1) h(θ)= H(θ,x)fθ(x) dx = 0, ZX where θ Θ Rdθ is a parameter vector and f (x), x Rdx , is a density ∈ ⊂ θ ∈X ⊂ function depending on θ. The dθ and dx denote the dimensions of θ and x, arXiv:1011.2587v1 [math.ST] 11 Nov 2010 respectively. -
A New Approach for Dynamic Stochastic Fractal Search with Fuzzy Logic for Parameter Adaptation
fractal and fractional Article A New Approach for Dynamic Stochastic Fractal Search with Fuzzy Logic for Parameter Adaptation Marylu L. Lagunes, Oscar Castillo * , Fevrier Valdez , Jose Soria and Patricia Melin Tijuana Institute of Technology, Calzada Tecnologico s/n, Fracc. Tomas Aquino, Tijuana 22379, Mexico; [email protected] (M.L.L.); [email protected] (F.V.); [email protected] (J.S.); [email protected] (P.M.) * Correspondence: [email protected] Abstract: Stochastic fractal search (SFS) is a novel method inspired by the process of stochastic growth in nature and the use of the fractal mathematical concept. Considering the chaotic stochastic diffusion property, an improved dynamic stochastic fractal search (DSFS) optimization algorithm is presented. The DSFS algorithm was tested with benchmark functions, such as the multimodal, hybrid, and composite functions, to evaluate the performance of the algorithm with dynamic parameter adaptation with type-1 and type-2 fuzzy inference models. The main contribution of the article is the utilization of fuzzy logic in the adaptation of the diffusion parameter in a dynamic fashion. This parameter is in charge of creating new fractal particles, and the diversity and iteration are the input information used in the fuzzy system to control the values of diffusion. Keywords: fractal search; fuzzy logic; parameter adaptation; CEC 2017 Citation: Lagunes, M.L.; Castillo, O.; Valdez, F.; Soria, J.; Melin, P. A New Approach for Dynamic Stochastic 1. Introduction Fractal Search with Fuzzy Logic for Metaheuristic algorithms are applied to optimization problems due to their charac- Parameter Adaptation. Fractal Fract. teristics that help in searching for the global optimum, while simple heuristics are mostly 2021, 5, 33. -
Introduction to Stochastic Processes - Lecture Notes (With 33 Illustrations)
Introduction to Stochastic Processes - Lecture Notes (with 33 illustrations) Gordan Žitković Department of Mathematics The University of Texas at Austin Contents 1 Probability review 4 1.1 Random variables . 4 1.2 Countable sets . 5 1.3 Discrete random variables . 5 1.4 Expectation . 7 1.5 Events and probability . 8 1.6 Dependence and independence . 9 1.7 Conditional probability . 10 1.8 Examples . 12 2 Mathematica in 15 min 15 2.1 Basic Syntax . 15 2.2 Numerical Approximation . 16 2.3 Expression Manipulation . 16 2.4 Lists and Functions . 17 2.5 Linear Algebra . 19 2.6 Predefined Constants . 20 2.7 Calculus . 20 2.8 Solving Equations . 22 2.9 Graphics . 22 2.10 Probability Distributions and Simulation . 23 2.11 Help Commands . 24 2.12 Common Mistakes . 25 3 Stochastic Processes 26 3.1 The canonical probability space . 27 3.2 Constructing the Random Walk . 28 3.3 Simulation . 29 3.3.1 Random number generation . 29 3.3.2 Simulation of Random Variables . 30 3.4 Monte Carlo Integration . 33 4 The Simple Random Walk 35 4.1 Construction . 35 4.2 The maximum . 36 1 CONTENTS 5 Generating functions 40 5.1 Definition and first properties . 40 5.2 Convolution and moments . 42 5.3 Random sums and Wald’s identity . 44 6 Random walks - advanced methods 48 6.1 Stopping times . 48 6.2 Wald’s identity II . 50 6.3 The distribution of the first hitting time T1 .......................... 52 6.3.1 A recursive formula . 52 6.3.2 Generating-function approach . -
Stochastic Approximation for the Multivariate and the Functional Median 3 It Can Not Be Updated Simply If the Data Arrive Online
STOCHASTIC APPROXIMATION TO THE MULTIVARIATE AND THE FUNCTIONAL MEDIAN Submitted to COMPSTAT 2010 Herv´eCardot1, Peggy C´enac1, and Mohamed Chaouch2 1 Institut de Math´ematiques de Bourgogne, UMR 5584 CNRS, Universit´ede Bourgogne, 9, Av. A. Savary - B.P. 47 870, 21078 Dijon, France [email protected], [email protected] 2 EDF - Recherche et D´eveloppement, ICAME-SOAD 1 Av. G´en´eralde Gaulle, 92141 Clamart, France [email protected] Abstract. We propose a very simple algorithm in order to estimate the geometric median, also called spatial median, of multivariate (Small, 1990) or functional data (Gervini, 2008) when the sample size is large. A simple and fast iterative approach based on the Robbins-Monro algorithm (Duflo, 1997) as well as its averaged version (Polyak and Juditsky, 1992) are shown to be effective for large samples of high dimension data. They are very fast and only require O(Nd) elementary operations, where N is the sample size and d is the dimension of data. The averaged approach is shown to be more effective and less sensitive to the tuning parameter. The ability of this new estimator to estimate accurately and rapidly (about thirty times faster than the classical estimator) the geometric median is illustrated on a large sample of 18902 electricity consumption curves measured every half an hour during one week. Keywords: Geometric quantiles, High dimension data, Online estimation al- gorithm, Robustness, Robbins-Monro, Spatial median, Stochastic gradient averaging 1 Introduction Estimation of the median of univariate and multivariate data has given rise to many publications in robust statistics, data mining, signal processing and information theory. -
1 Stochastic Processes and Their Classification
1 1 STOCHASTIC PROCESSES AND THEIR CLASSIFICATION 1.1 DEFINITION AND EXAMPLES Definition 1. Stochastic process or random process is a collection of random variables ordered by an index set. ☛ Example 1. Random variables X0;X1;X2;::: form a stochastic process ordered by the discrete index set f0; 1; 2;::: g: Notation: fXn : n = 0; 1; 2;::: g: ☛ Example 2. Stochastic process fYt : t ¸ 0g: with continuous index set ft : t ¸ 0g: The indices n and t are often referred to as "time", so that Xn is a descrete-time process and Yt is a continuous-time process. Convention: the index set of a stochastic process is always infinite. The range (possible values) of the random variables in a stochastic process is called the state space of the process. We consider both discrete-state and continuous-state processes. Further examples: ☛ Example 3. fXn : n = 0; 1; 2;::: g; where the state space of Xn is f0; 1; 2; 3; 4g representing which of four types of transactions a person submits to an on-line data- base service, and time n corresponds to the number of transactions submitted. ☛ Example 4. fXn : n = 0; 1; 2;::: g; where the state space of Xn is f1; 2g re- presenting whether an electronic component is acceptable or defective, and time n corresponds to the number of components produced. ☛ Example 5. fYt : t ¸ 0g; where the state space of Yt is f0; 1; 2;::: g representing the number of accidents that have occurred at an intersection, and time t corresponds to weeks. ☛ Example 6. fYt : t ¸ 0g; where the state space of Yt is f0; 1; 2; : : : ; sg representing the number of copies of a software product in inventory, and time t corresponds to days. -
Simulation Methods for Robust Risk Assessment and the Distorted Mix
Simulation Methods for Robust Risk Assessment and the Distorted Mix Approach Sojung Kim Stefan Weber Leibniz Universität Hannover September 9, 2020∗ Abstract Uncertainty requires suitable techniques for risk assessment. Combining stochastic ap- proximation and stochastic average approximation, we propose an efficient algorithm to compute the worst case average value at risk in the face of tail uncertainty. Dependence is modelled by the distorted mix method that flexibly assigns different copulas to different regions of multivariate distributions. We illustrate the application of our approach in the context of financial markets and cyber risk. Keywords: Uncertainty; average value at risk; distorted mix method; stochastic approximation; stochastic average approximation; financial markets; cyber risk. 1 Introduction Capital requirements are an instrument to limit the downside risk of financial companies. They constitute an important part of banking and insurance regulation, for example, in the context of Basel III, Solvency II, and the Swiss Solvency test. Their purpose is to provide a buffer to protect policy holders, customers, and creditors. Within complex financial networks, capital requirements also mitigate systemic risks. The quantitative assessment of the downside risk of financial portfolios is a fundamental, but arduous task. The difficulty of estimating downside risk stems from the fact that extreme events are rare; in addition, portfolio downside risk is largely governed by the tail dependence of positions which can hardly be estimated from data and is typically unknown. Tail dependence is a major source of model uncertainty when assessing the downside risk. arXiv:2009.03653v1 [q-fin.RM] 8 Sep 2020 In practice, when extracting information from data, various statistical tools are applied for fitting both the marginals and the copulas – either (semi-)parametrically or empirically. -
An Empirical Bayes Procedure for the Selection of Gaussian Graphical Models
An empirical Bayes procedure for the selection of Gaussian graphical models Sophie Donnet CEREMADE Universite´ Paris Dauphine, France Jean-Michel Marin∗ Institut de Mathematiques´ et Modelisation´ de Montpellier Universite´ Montpellier 2, France Abstract A new methodology for model determination in decomposable graphical Gaussian models (Dawid and Lauritzen, 1993) is developed. The Bayesian paradigm is used and, for each given graph, a hyper inverse Wishart prior distribution on the covariance matrix is considered. This prior distribution depends on hyper-parameters. It is well-known that the models’s posterior distribution is sensitive to the specification of these hyper-parameters and no completely satisfactory method is registered. In order to avoid this problem, we suggest adopting an empirical Bayes strategy, that is a strategy for which the values of the hyper-parameters are determined using the data. Typically, the hyper-parameters are fixed to their maximum likelihood estimations. In order to calculate these maximum likelihood estimations, we suggest a Markov chain Monte Carlo version of the Stochastic Approximation EM algorithm. Moreover, we introduce a new sampling scheme in the space of graphs that improves the add and delete proposal of Armstrong et al. (2009). We illustrate the efficiency of this new scheme on simulated and real datasets. Keywords: Gaussian graphical models, decomposable models, empirical Bayes, Stochas- arXiv:1003.5851v2 [stat.CO] 23 May 2011 tic Approximation EM, Markov Chain Monte Carlo ∗Corresponding author: [email protected] 1 1 Gaussian graphical models in a Bayesian Context Statistical applications in genetics, sociology, biology , etc often lead to complicated interaction patterns between variables. -
Stochastic Approximation of Score Functions for Gaussian Processes
The Annals of Applied Statistics 2013, Vol. 7, No. 2, 1162–1191 DOI: 10.1214/13-AOAS627 c Institute of Mathematical Statistics, 2013 STOCHASTIC APPROXIMATION OF SCORE FUNCTIONS FOR GAUSSIAN PROCESSES1 By Michael L. Stein2, Jie Chen3 and Mihai Anitescu3 University of Chicago, Argonne National Laboratory and Argonne National Laboratory We discuss the statistical properties of a recently introduced un- biased stochastic approximation to the score equations for maximum likelihood calculation for Gaussian processes. Under certain condi- tions, including bounded condition number of the covariance matrix, the approach achieves O(n) storage and nearly O(n) computational effort per optimization step, where n is the number of data sites. Here, we prove that if the condition number of the covariance matrix is bounded, then the approximate score equations are nearly optimal in a well-defined sense. Therefore, not only is the approximation ef- ficient to compute, but it also has comparable statistical properties to the exact maximum likelihood estimates. We discuss a modifi- cation of the stochastic approximation in which design elements of the stochastic terms mimic patterns from a 2n factorial design. We prove these designs are always at least as good as the unstructured design, and we demonstrate through simulation that they can pro- duce a substantial improvement over random designs. Our findings are validated by numerical experiments on simulated data sets of up to 1 million observations. We apply the approach to fit a space–time model to over 80,000 observations of total column ozone contained in the latitude band 40◦–50◦N during April 2012. -
Monte Carlo Sampling Methods
[1] Monte Carlo Sampling Methods Jasmina L. Vujic Nuclear Engineering Department University of California, Berkeley Email: [email protected] phone: (510) 643-8085 fax: (510) 643-9685 UCBNE, J. Vujic [2] Monte Carlo Monte Carlo is a computational technique based on constructing a random process for a problem and carrying out a NUMERICAL EXPERIMENT by N-fold sampling from a random sequence of numbers with a PRESCRIBED probability distribution. x - random variable N 1 xˆ = ---- x N∑ i i = 1 Xˆ - the estimated or sample mean of x x - the expectation or true mean value of x If a problem can be given a PROBABILISTIC interpretation, then it can be modeled using RANDOM NUMBERS. UCBNE, J. Vujic [3] Monte Carlo • Monte Carlo techniques came from the complicated diffusion problems that were encountered in the early work on atomic energy. • 1772 Compte de Bufon - earliest documented use of random sampling to solve a mathematical problem. • 1786 Laplace suggested that π could be evaluated by random sampling. • Lord Kelvin used random sampling to aid in evaluating time integrals associated with the kinetic theory of gases. • Enrico Fermi was among the first to apply random sampling methods to study neutron moderation in Rome. • 1947 Fermi, John von Neuman, Stan Frankel, Nicholas Metropolis, Stan Ulam and others developed computer-oriented Monte Carlo methods at Los Alamos to trace neutrons through fissionable materials UCBNE, J. Vujic Monte Carlo [4] Monte Carlo methods can be used to solve: a) The problems that are stochastic (probabilistic) by nature: - particle transport, - telephone and other communication systems, - population studies based on the statistics of survival and reproduction. -
Stochastic Approximation Methods and Applications in Financial Optimization Problems
Stochastic Approximation Methods And Applications in Financial Optimization Problems by Chao Zhuang (Under the direction of Qing Zhang) Abstract Optimizations play an increasingly indispensable role in financial decisions and financial models. Many problems in mathematical finance, such as asset allocation, trading strategy, and derivative pricing, are now routinely and efficiently approached using optimization. Not until recently have stochastic approximation methods been applied to solve optimization problems in finance. This dissertation is concerned with stochastic approximation algorithms and their appli- cations in financial optimization problems. The first part of this dissertation concerns trading a mean-reverting asset. The strategy is to determine a low price to buy and a high price to sell so that the expected return is maximized. Slippage cost is imposed on each transaction. Our effort is devoted to developing a recursive stochastic approximation type algorithm to esti- mate the desired selling and buying prices. In the second part of this dissertation we consider the trailing stop strategy. Trailing stops are often used in stock trading to limit the maximum of a possible loss and to lock in a profit. We develop stochastic approximation algorithms to estimate the optimal trailing stop percentage. A modification using projection is devel- oped to ensure that the approximation sequence constructed stays in a reasonable range. In both parts, we also study the convergence and the rate of convergence. Simulations and real market data are used to demonstrate the performance of the proposed algorithms. The advantage of using stochastic approximation in stock trading is that the underlying asset is model free. Only observed stock prices are required, so it can be performed on line to provide guidelines for stock trading. -
Random Numbers and Stochastic Simulation
Stochastic Simulation and Randomness Random Number Generators Quasi-Random Sequences Scientific Computing: An Introductory Survey Chapter 13 – Random Numbers and Stochastic Simulation Prof. Michael T. Heath Department of Computer Science University of Illinois at Urbana-Champaign Copyright c 2002. Reproduction permitted for noncommercial, educational use only. Michael T. Heath Scientific Computing 1 / 17 Stochastic Simulation and Randomness Random Number Generators Quasi-Random Sequences Stochastic Simulation Stochastic simulation mimics or replicates behavior of system by exploiting randomness to obtain statistical sample of possible outcomes Because of randomness involved, simulation methods are also known as Monte Carlo methods Such methods are useful for studying Nondeterministic (stochastic) processes Deterministic systems that are too complicated to model analytically Deterministic problems whose high dimensionality makes standard discretizations infeasible (e.g., Monte Carlo integration) < interactive example > < interactive example > Michael T. Heath Scientific Computing 2 / 17 Stochastic Simulation and Randomness Random Number Generators Quasi-Random Sequences Stochastic Simulation, continued Two main requirements for using stochastic simulation methods are Knowledge of relevant probability distributions Supply of random numbers for making random choices Knowledge of relevant probability distributions depends on theoretical or empirical information about physical system being simulated By simulating large number of trials, probability -
Stochastic Approximation Algorithm
Learning in Complex Systems Spring 2011 Lecture Notes Nahum Shimkin 5 The Stochastic Approximation Algorithm 5.1 Stochastic Processes { Some Basic Concepts 5.1.1 Random Variables and Random Sequences Let (; F;P ) be a probability space, namely: { is the sample space. { F is the event space. Its elements are subsets of , and it is required to be a σ- algebra (includes ; and ; includes all countable union of its members; includes all complements of its members). { P is the probability measure (assigns a probability in [0,1] to each element of F, with the usual properties: P () = 1, countably additive). A random variable (RV) X on (; F) is a function X : ! R, with values X(!). It is required to be measurable on F, namely, all sets of the form f! : X(!) · ag are events in F. A vector-valued RV is a vector of RVs. Equivalently, it is a function X : ! Rd, with similar measurability requirement. d A random sequence, or a discrete-time stochastic process, is a sequence (Xn)n¸0 of R -valued RVs, which are all de¯ned on the same probability space. 1 5.1.2 Convergence of Random Variables A random sequence may converge to a random variable, say to X. There are several useful notions of convergence: 1. Almost sure convergence (or: convergence with probability 1): a:s: Xn ! X if P f lim Xn = Xg = 1 : n!1 2. Convergence in probability: p Xn ! X if lim P (jXn ¡ Xj > ²) = 0 ; 8² > 0 : n!1 3. Mean-squares convergence (convergence in L2): 2 L 2 Xn ! X if EjXn ¡ X1j ! 0 : 4.