Program on Quasi-Monte Carlo and High-Dimensional Sampling Methods for Applied Mathematics Opening Workshop, QMC and Thinning for Empirical Datasets - Mike Giles, Sep 1, 2017
In this talk we consider the question of how to use QMC with an empirical dataset, such as a set of points generated by MCMC. Using ideas from partitioning for parallel computing, we apply recursive bisection to reorder the points, and then interleave the bits of the QMC coordinates to select the appropriate point from the dataset. Numerical tests show that in the case of known distributions this is almost as effective as applying QMC directly to the original distribution. The same recursive bisection can also be used to thin the dataset, by recursively bisecting down to many small subsets of points, and then randomly selecting one point from each subset. This makes it possible to reduce the size of the dataset greatly without significantly increasing the overall error. Co-author: Fei Xie
One of the central tasks in computational mathematics and statistics is to accurately approximate unknown target functions. This is typically done with the help of data — samples of the unknown functions. The emergence of Big Data presents both opportunities and challenges. On one hand, big data introduces more information about the unknowns and, in principle, allows us to create more accurate models. On the other hand, data storage and processing become highly challenging. In this talk, we present a set of sequential algorithms for function approximation in high dimensions with large data sets. The algorithms are of iterative nature and involve only vector operations. They use one data sample at each step and can handle dynamic/stream data. We present both the numerical algorithms, which are easy to implement, as well as rigorous analysis for their theoretical foundation.
We will describe and analyze accurate and efficient numerical algorithms to interpolate and approximate the integral of multivariate functions. The algorithms can be applied when we are given the function values at an arbitrary positioned, and usually small, existing sparse set of function values (samples), and additional samples are impossible, or difficult (e.g. expensive) to obtain. The methods are based on local, and global, tensor-product sparse quasi-interpolation methods that are exact for a class of sparse multivariate orthogonal polynomials.
A fundamental numerical problem in many sciences is to compute integrals. These integrals can often be expressed as expectations and then approximated by sampling methods. Monte Carlo sampling is very competitive in high dimensions, but has a slow rate of convergence. One reason for this slowness is that the MC points form clusters and gaps. Quasi-Monte Carlo methods greatly reduce such clusters and gaps, and under modest smoothness demands on the integrand they can greatly improve accuracy. This can even take place in problems of surprisingly high dimension. This talk will introduce the basics of QMC and randomized QMC. It will include discrepancy and the Koksma-Hlawka inequality, some digital constructions and some randomized QMC methods that allow error estimation and sometimes bring improved accuracy.
We present recent result on the numerical analysis of Quasi Monte-Carlo quadrature methods, applied to forward and inverse uncertainty quantification for elliptic and parabolic PDEs. Particular attention will be placed on Higher
-Order QMC, the stable and efficient generation of
interlaced polynomial lattice rules, and the numerical analysis of multilevel QMC Finite Element discretizations with applications to computational uncertainty quantification.
Sequential quasi-Monte Carlo (SQMC) is a quasi-Monte Carlo (QMC) version of sequential Monte Carlo (or particle filtering), a popular class of Monte Carlo techniques used to carry out inference in state space models. In this talk I will first review the SQMC methodology as well as some theoretical results. Although SQMC converges faster than the usual Monte Carlo error rate its performance deteriorates quickly as the dimension of the hidden variable increases. However, I will show with an example that SQMC may perform well for some "high" dimensional problems. I will conclude this talk with some open problems and potential applications of SQMC in complicated settings.
The generation of Gaussian random fields over a physical domain is a challenging problem in computational mathematics, especially when the correlation length is short and the field is rough. The traditional approach is to make use of a truncated Karhunen-Loeve (KL) expansion, but the generation of even a single realisation of the field may then be effectively beyond reach (especially for 3-dimensional domains) if the need is to obtain an expected L2 error of say 5%, because of the potentially very slow convergence of the KL expansion. In this talk, based on joint work with Ivan Graham, Frances Kuo, Dirk Nuyens, and Rob Scheichl, a completely different approach is used, in which the field is initially generated at a regular grid on a 2- or 3-dimensional rectangle that contains the physical domain, and then possibly interpolated to obtain the field at other points. In that case there is no need for any truncation. Rather the main problem becomes the factorisation of a large dense matrix. For this we use circulant embedding and FFT ideas. Quasi-Monte Carlo integration is then used to evaluate the expected value of some functional of the finite-element solution of an elliptic PDE with a random field as input.
Markov chain Monte Carlo (MCMC) methods are popularly used in Bayesian computation. However, they need large number of samples for convergence which can become costly when the posterior distribution is expensive to evaluate. Deterministic sampling techniques such as Quasi-Monte Carlo (QMC) can be a useful alternative to MCMC, but the existing QMC methods are mainly developed only for sampling from unit hypercubes. Unfortunately, the posterior distributions can be highly correlated and nonlinear making them occupy very little space inside a hypercube. Thus, most of the samples from QMC can get wasted. The QMC samples can be saved if they can be pulled towards the high probability regions of the posterior distribution using inverse probability transforms. But this can be done only when the distribution function is known, which is rarely the case in Bayesian problems. In this talk, I will discuss a deterministic sampling technique, known as minimum energy designs, which can directly sample from the posterior distributions.
One of the central tasks in computational mathematics and statistics is to accurately approximate unknown target functions. This is typically done with the help of data — samples of the unknown functions. The emergence of Big Data presents both opportunities and challenges. On one hand, big data introduces more information about the unknowns and, in principle, allows us to create more accurate models. On the other hand, data storage and processing become highly challenging. In this talk, we present a set of sequential algorithms for function approximation in high dimensions with large data sets. The algorithms are of iterative nature and involve only vector operations. They use one data sample at each step and can handle dynamic/stream data. We present both the numerical algorithms, which are easy to implement, as well as rigorous analysis for their theoretical foundation.
We will describe and analyze accurate and efficient numerical algorithms to interpolate and approximate the integral of multivariate functions. The algorithms can be applied when we are given the function values at an arbitrary positioned, and usually small, existing sparse set of function values (samples), and additional samples are impossible, or difficult (e.g. expensive) to obtain. The methods are based on local, and global, tensor-product sparse quasi-interpolation methods that are exact for a class of sparse multivariate orthogonal polynomials.
A fundamental numerical problem in many sciences is to compute integrals. These integrals can often be expressed as expectations and then approximated by sampling methods. Monte Carlo sampling is very competitive in high dimensions, but has a slow rate of convergence. One reason for this slowness is that the MC points form clusters and gaps. Quasi-Monte Carlo methods greatly reduce such clusters and gaps, and under modest smoothness demands on the integrand they can greatly improve accuracy. This can even take place in problems of surprisingly high dimension. This talk will introduce the basics of QMC and randomized QMC. It will include discrepancy and the Koksma-Hlawka inequality, some digital constructions and some randomized QMC methods that allow error estimation and sometimes bring improved accuracy.
We present recent result on the numerical analysis of Quasi Monte-Carlo quadrature methods, applied to forward and inverse uncertainty quantification for elliptic and parabolic PDEs. Particular attention will be placed on Higher
-Order QMC, the stable and efficient generation of
interlaced polynomial lattice rules, and the numerical analysis of multilevel QMC Finite Element discretizations with applications to computational uncertainty quantification.
Sequential quasi-Monte Carlo (SQMC) is a quasi-Monte Carlo (QMC) version of sequential Monte Carlo (or particle filtering), a popular class of Monte Carlo techniques used to carry out inference in state space models. In this talk I will first review the SQMC methodology as well as some theoretical results. Although SQMC converges faster than the usual Monte Carlo error rate its performance deteriorates quickly as the dimension of the hidden variable increases. However, I will show with an example that SQMC may perform well for some "high" dimensional problems. I will conclude this talk with some open problems and potential applications of SQMC in complicated settings.
The generation of Gaussian random fields over a physical domain is a challenging problem in computational mathematics, especially when the correlation length is short and the field is rough. The traditional approach is to make use of a truncated Karhunen-Loeve (KL) expansion, but the generation of even a single realisation of the field may then be effectively beyond reach (especially for 3-dimensional domains) if the need is to obtain an expected L2 error of say 5%, because of the potentially very slow convergence of the KL expansion. In this talk, based on joint work with Ivan Graham, Frances Kuo, Dirk Nuyens, and Rob Scheichl, a completely different approach is used, in which the field is initially generated at a regular grid on a 2- or 3-dimensional rectangle that contains the physical domain, and then possibly interpolated to obtain the field at other points. In that case there is no need for any truncation. Rather the main problem becomes the factorisation of a large dense matrix. For this we use circulant embedding and FFT ideas. Quasi-Monte Carlo integration is then used to evaluate the expected value of some functional of the finite-element solution of an elliptic PDE with a random field as input.
Markov chain Monte Carlo (MCMC) methods are popularly used in Bayesian computation. However, they need large number of samples for convergence which can become costly when the posterior distribution is expensive to evaluate. Deterministic sampling techniques such as Quasi-Monte Carlo (QMC) can be a useful alternative to MCMC, but the existing QMC methods are mainly developed only for sampling from unit hypercubes. Unfortunately, the posterior distributions can be highly correlated and nonlinear making them occupy very little space inside a hypercube. Thus, most of the samples from QMC can get wasted. The QMC samples can be saved if they can be pulled towards the high probability regions of the posterior distribution using inverse probability transforms. But this can be done only when the distribution function is known, which is rarely the case in Bayesian problems. In this talk, I will discuss a deterministic sampling technique, known as minimum energy designs, which can directly sample from the posterior distributions.
The standard Galerkin formulation of the acoustic wave propagation, governed by the Helmholtz partial differential equation (PDE), is indefinite for large wavenumbers. However, the Helmholtz PDE is in general not indefinite. The lack of coercivity (indefiniteness) is one of the major difficulties for approximation and simulation of heterogeneous media wave propagation models, including application to stochastic wave propagation Quasi Monte Carlo (QMC) analysis. We will present a new class of sign-definite continuous and discrete preconditioned FEM Helmholtz wave propagation models.
Multidimensional integrals may be approximated by weighted averages of integrand values. Quasi-Monte Carlo (QMC) methods are more accurate than simple Monte Carlo methods because they carefully choose where to evaluate the integrand. This tutorial focuses on how quickly QMC methods converge to the correct answer as the number of integrand values increases. The answer may depend on the smoothness of the integrand and the sophistication of the QMC method. QMC error analysis may assumes the integrand belongs to a reproducing kernel Hilbert space or may assume that the integrand is an instance of a stochastic process with known covariance structure. These two approaches have interesting parallels. This tutorial also explores how the computational cost of achieving a good approximation to the integral depends on the dimension of the domain of the integrand. Finally, this tutorial explores methods for determining how many integrand values are needed to satisfy the error tolerance. Relevant software is described.
Many mathematical models use a large number of poorly-known parameters as inputs. Quantifying the influence of each of these parameters is one of the aims of sensitivity analysis. Global Sensitivity Analysis is an important paradigm for understanding model behavior, characterizing uncertainty, improving model calibration, etc. Inputs’ uncertainty is modeled by a probability distribution. There exist various measures built in that paradigm. This tutorial focuses on the so-called Sobol’ indices, based on functional variance analysis. Estimation procedures will be presented, and the choice of the designs of experiments these procedures are based on will be discussed. As Sobol’ indices have no clear interpretation in the presence of statistical dependences between inputs, it also seems promising to measure sensitivity with Shapley effects, based on the notion of Shapley value, which is a solution concept in cooperative game theory.
In this talk, we discuss some recent advances in probabilistic schemes for high-dimensional PIDEs. It is known that traditional PDE solvers, e.g., finite element, finite difference methods, do not scale well with the increase of dimension. The idea of probabilistic schemes is to link a wide class of nonlinear parabolic PIDEs to stochastic Levy processes based on nonlinear version of the Feynman-Kac theory. As such, the solution of the PIDE can be represented by a conditional expectation (i.e., a high-dimensional integral) with respect to a stochastic dynamical system driven by Levy processes. In other words, we can solve the PIDEs by performing high-dimensional numerical integration. A variety of quadrature methods could be applied, including MC, QMC, sparse grids, etc. The probabilistic schemes have been used in many application problems, e.g., particle transport in plasmas (e.g., Vlasov-Fokker-Planck equations), nonlinear filtering (e.g., Zakai equations), and option pricing, etc.
We combined: low-rank tensor techniques and FFT to compute kriging, estimate variance, compute conditional covariance. We are able to solve 3D problems with very high resolution
In this tutorial I will provide a survey of recent research efforts on the application of QMC methods to PDEs with random coefficients. Such PDE problems occur in the area of uncertainty quantification. A prime example is the flow of water through a disordered porous medium. There is a huge body of literature on this topic using a variety of methods. QMC methods are relatively new to this application area. The aim of this tutorial is to provide an entry point for QMC experts wanting to start research in this direction, for PDE analysts and practitioners wanting to tap into contemporary QMC theory and methods, and for anyone else who sees how to cross-fertilize the ideas to other application areas.
A multi-objective optimization framework for a second order traffic flow mode...Guillaume Costeseque
This slides deal with a common work with Paola Goatin (Inria Sophia-Antipolis), Simone Göttlich and Oliver Kolb (Universität Mannheim) about Riemann solvers for the ARZ model on a junction
The standard Galerkin formulation of the acoustic wave propagation, governed by the Helmholtz partial differential equation (PDE), is indefinite for large wavenumbers. However, the Helmholtz PDE is in general not indefinite. The lack of coercivity (indefiniteness) is one of the major difficulties for approximation and simulation of heterogeneous media wave propagation models, including application to stochastic wave propagation Quasi Monte Carlo (QMC) analysis. We will present a new class of sign-definite continuous and discrete preconditioned FEM Helmholtz wave propagation models.
Multidimensional integrals may be approximated by weighted averages of integrand values. Quasi-Monte Carlo (QMC) methods are more accurate than simple Monte Carlo methods because they carefully choose where to evaluate the integrand. This tutorial focuses on how quickly QMC methods converge to the correct answer as the number of integrand values increases. The answer may depend on the smoothness of the integrand and the sophistication of the QMC method. QMC error analysis may assumes the integrand belongs to a reproducing kernel Hilbert space or may assume that the integrand is an instance of a stochastic process with known covariance structure. These two approaches have interesting parallels. This tutorial also explores how the computational cost of achieving a good approximation to the integral depends on the dimension of the domain of the integrand. Finally, this tutorial explores methods for determining how many integrand values are needed to satisfy the error tolerance. Relevant software is described.
Many mathematical models use a large number of poorly-known parameters as inputs. Quantifying the influence of each of these parameters is one of the aims of sensitivity analysis. Global Sensitivity Analysis is an important paradigm for understanding model behavior, characterizing uncertainty, improving model calibration, etc. Inputs’ uncertainty is modeled by a probability distribution. There exist various measures built in that paradigm. This tutorial focuses on the so-called Sobol’ indices, based on functional variance analysis. Estimation procedures will be presented, and the choice of the designs of experiments these procedures are based on will be discussed. As Sobol’ indices have no clear interpretation in the presence of statistical dependences between inputs, it also seems promising to measure sensitivity with Shapley effects, based on the notion of Shapley value, which is a solution concept in cooperative game theory.
In this talk, we discuss some recent advances in probabilistic schemes for high-dimensional PIDEs. It is known that traditional PDE solvers, e.g., finite element, finite difference methods, do not scale well with the increase of dimension. The idea of probabilistic schemes is to link a wide class of nonlinear parabolic PIDEs to stochastic Levy processes based on nonlinear version of the Feynman-Kac theory. As such, the solution of the PIDE can be represented by a conditional expectation (i.e., a high-dimensional integral) with respect to a stochastic dynamical system driven by Levy processes. In other words, we can solve the PIDEs by performing high-dimensional numerical integration. A variety of quadrature methods could be applied, including MC, QMC, sparse grids, etc. The probabilistic schemes have been used in many application problems, e.g., particle transport in plasmas (e.g., Vlasov-Fokker-Planck equations), nonlinear filtering (e.g., Zakai equations), and option pricing, etc.
We combined: low-rank tensor techniques and FFT to compute kriging, estimate variance, compute conditional covariance. We are able to solve 3D problems with very high resolution
In this tutorial I will provide a survey of recent research efforts on the application of QMC methods to PDEs with random coefficients. Such PDE problems occur in the area of uncertainty quantification. A prime example is the flow of water through a disordered porous medium. There is a huge body of literature on this topic using a variety of methods. QMC methods are relatively new to this application area. The aim of this tutorial is to provide an entry point for QMC experts wanting to start research in this direction, for PDE analysts and practitioners wanting to tap into contemporary QMC theory and methods, and for anyone else who sees how to cross-fertilize the ideas to other application areas.
Program on Quasi-Monte Carlo and High-Dimensional Sampling Methods for Applie...
Similar to Program on Quasi-Monte Carlo and High-Dimensional Sampling Methods for Applied Mathematics Opening Workshop, QMC and Thinning for Empirical Datasets - Mike Giles, Sep 1, 2017
A multi-objective optimization framework for a second order traffic flow mode...Guillaume Costeseque
This slides deal with a common work with Paola Goatin (Inria Sophia-Antipolis), Simone Göttlich and Oliver Kolb (Universität Mannheim) about Riemann solvers for the ARZ model on a junction
How is the algorithmic thinking is applied in (mathematical) finance? What are the main differences in approaches?
Some basics of theoretical computer science are explained, some hands-on applications to finance are shown together with the industry practice.
Localized methods for diffusions in large graphsDavid Gleich
I describe a few ongoing research projects on diffusions in large graphs and how we can create efficient matrix computations in order to determine them efficiently.
Here we overview the problem of clustering, why it's so different from supervised learning problems, how to select the number of clusters. We discuss main approaches to perform clustering: k-Means, hierarchical clustering, DBSCAN.
The Gaussian or normal distribution is one of the most widely used in statistics. Estimating its parameters using
Bayesian inference and conjugate priors is also widely used. The use of conjugate priors allows all the results to be
derived in closed form. Unfortunately, different books use different conventions on how to parameterize the various
distributions (e.g., put the prior on the precision or the variance, use an inverse gamma or inverse chi-squared, etc),
which can be very confusing for the student. In this report, we summarize all of the most commonly used forms. We
provide detailed derivations for some of these results; the rest can be obtained by simple reparameterization. See the
appendix for the definition the distributions that are used.
My talk in the International Conference on Computational Finance 2019 (ICCF2019). The talk is about designing new efficient methods for option pricing under the rough Bergomi model.
We compute polynomial based surrogates for all components of the solution of the Navier-Stokes equation. We compress this surrogate on the fly to reduce cubic computational complexity to almost linear. All these surrogates are used to quantify uncertainties in numerical aerodynamics.
Similar to Program on Quasi-Monte Carlo and High-Dimensional Sampling Methods for Applied Mathematics Opening Workshop, QMC and Thinning for Empirical Datasets - Mike Giles, Sep 1, 2017 (20)
Recently, the machine learning community has expressed strong interest in applying latent variable modeling strategies to causal inference problems with unobserved confounding. Here, I discuss one of the big debates that occurred over the past year, and how we can move forward. I will focus specifically on the failure of point identification in this setting, and discuss how this can be used to design flexible sensitivity analyses that cleanly separate identified and unidentified components of the causal model.
I will discuss paradigmatic statistical models of inference and learning from high dimensional data, such as sparse PCA and the perceptron neural network, in the sub-linear sparsity regime. In this limit the underlying hidden signal, i.e., the low-rank matrix in PCA or the neural network weights, has a number of non-zero components that scales sub-linearly with the total dimension of the vector. I will provide explicit low-dimensional variational formulas for the asymptotic mutual information between the signal and the data in suitable sparse limits. In the setting of support recovery these formulas imply sharp 0-1 phase transitions for the asymptotic minimum mean-square-error (or generalization error in the neural network setting). A similar phase transition was analyzed recently in the context of sparse high-dimensional linear regression by Reeves et al.
Many different measurement techniques are used to record neural activity in the brains of different organisms, including fMRI, EEG, MEG, lightsheet microscopy and direct recordings with electrodes. Each of these measurement modes have their advantages and disadvantages concerning the resolution of the data in space and time, the directness of measurement of the neural activity and which organisms they can be applied to. For some of these modes and for some organisms, significant amounts of data are now available in large standardized open-source datasets. I will report on our efforts to apply causal discovery algorithms to, among others, fMRI data from the Human Connectome Project, and to lightsheet microscopy data from zebrafish larvae. In particular, I will focus on the challenges we have faced both in terms of the nature of the data and the computational features of the discovery algorithms, as well as the modeling of experimental interventions.
Bayesian Additive Regression Trees (BART) has been shown to be an effective framework for modeling nonlinear regression functions, with strong predictive performance in a variety of contexts. The BART prior over a regression function is defined by independent prior distributions on tree structure and leaf or end-node parameters. In observational data settings, Bayesian Causal Forests (BCF) has successfully adapted BART for estimating heterogeneous treatment effects, particularly in cases where standard methods yield biased estimates due to strong confounding.
We introduce BART with Targeted Smoothing, an extension which induces smoothness over a single covariate by replacing independent Gaussian leaf priors with smooth functions. We then introduce a new version of the Bayesian Causal Forest prior, which incorporates targeted smoothing for modeling heterogeneous treatment effects which vary smoothly over a target covariate. We demonstrate the utility of this approach by applying our model to a timely women's health and policy problem: comparing two dosing regimens for an early medical abortion protocol, where the outcome of interest is the probability of a successful early medical abortion procedure at varying gestational ages, conditional on patient covariates. We discuss the benefits of this approach in other women’s health and obstetrics modeling problems where gestational age is a typical covariate.
Difference-in-differences is a widely used evaluation strategy that draws causal inference from observational panel data. Its causal identification relies on the assumption of parallel trends, which is scale-dependent and may be questionable in some applications. A common alternative is a regression model that adjusts for the lagged dependent variable, which rests on the assumption of ignorability conditional on past outcomes. In the context of linear models, Angrist and Pischke (2009) show that the difference-in-differences and lagged-dependent-variable regression estimates have a bracketing relationship. Namely, for a true positive effect, if ignorability is correct, then mistakenly assuming parallel trends will overestimate the effect; in contrast, if the parallel trends assumption is correct, then mistakenly assuming ignorability will underestimate the effect. We show that the same bracketing relationship holds in general nonparametric (model-free) settings. We also extend the result to semiparametric estimation based on inverse probability weighting.
We develop sensitivity analyses for weak nulls in matched observational studies while allowing unit-level treatment effects to vary. In contrast to randomized experiments and paired observational studies, we show for general matched designs that over a large class of test statistics, any valid sensitivity analysis for the weak null must be unnecessarily conservative if Fisher's sharp null of no treatment effect for any individual also holds. We present a sensitivity analysis valid for the weak null, and illustrate why it is conservative if the sharp null holds through connections to inverse probability weighted estimators. An alternative procedure is presented that is asymptotically sharp if treatment effects are constant, and is valid for the weak null under additional assumptions which may be deemed reasonable by practitioners. The methods may be applied to matched observational studies constructed using any optimal without-replacement matching algorithm, allowing practitioners to assess robustness to hidden bias while allowing for treatment effect heterogeneity.
The world of health care is full of policy interventions: a state expands eligibility rules for its Medicaid program, a medical society changes its recommendations for screening frequency, a hospital implements a new care coordination program. After a policy change, we often want to know, “Did it work?” This is a causal question; we want to know whether the policy CAUSED outcomes to change. One popular way of estimating causal effects of policy interventions is a difference-in-differences study. In this controlled pre-post design, we measure the change in outcomes of people who are exposed to the new policy, comparing average outcomes before and after the policy is implemented. We contrast that change to the change over the same time period in people who were not exposed to the new policy. The differential change in the treated group’s outcomes, compared to the change in the comparison group’s outcomes, may be interpreted as the causal effect of the policy. To do so, we must assume that the comparison group’s outcome change is a good proxy for the treated group’s (counterfactual) outcome change in the absence of the policy. This conceptual simplicity and wide applicability in policy settings makes difference-in-differences an appealing study design. However, the apparent simplicity belies a thicket of conceptual, causal, and statistical complexity. In this talk, I will introduce the fundamentals of difference-in-differences studies and discuss recent innovations including key assumptions and ways to assess their plausibility, estimation, inference, and robustness checks.
We present recent advances and statistical developments for evaluating Dynamic Treatment Regimes (DTR), which allow the treatment to be dynamically tailored according to evolving subject-level data. Identification of an optimal DTR is a key component for precision medicine and personalized health care. Specific topics covered in this talk include several recent projects with robust and flexible methods developed for the above research area. We will first introduce a dynamic statistical learning method, adaptive contrast weighted learning (ACWL), which combines doubly robust semiparametric regression estimators with flexible machine learning methods. We will further develop a tree-based reinforcement learning (T-RL) method, which builds an unsupervised decision tree that maintains the nature of batch-mode reinforcement learning. Unlike ACWL, T-RL handles the optimization problem with multiple treatment comparisons directly through a purity measure constructed with augmented inverse probability weighted estimators. T-RL is robust, efficient and easy to interpret for the identification of optimal DTRs. However, ACWL seems more robust against tree-type misspecification than T-RL when the true optimal DTR is non-tree-type. At the end of this talk, we will also present a new Stochastic-Tree Search method called ST-RL for evaluating optimal DTRs.
A fundamental feature of evaluating causal health effects of air quality regulations is that air pollution moves through space, rendering health outcomes at a particular population location dependent upon regulatory actions taken at multiple, possibly distant, pollution sources. Motivated by studies of the public-health impacts of power plant regulations in the U.S., this talk introduces the novel setting of bipartite causal inference with interference, which arises when 1) treatments are defined on observational units that are distinct from those at which outcomes are measured and 2) there is interference between units in the sense that outcomes for some units depend on the treatments assigned to many other units. Interference in this setting arises due to complex exposure patterns dictated by physical-chemical atmospheric processes of pollution transport, with intervention effects framed as propagating across a bipartite network of power plants and residential zip codes. New causal estimands are introduced for the bipartite setting, along with an estimation approach based on generalized propensity scores for treatments on a network. The new methods are deployed to estimate how emission-reduction technologies implemented at coal-fired power plants causally affect health outcomes among Medicare beneficiaries in the U.S.
Laine Thomas presented information about how causal inference is being used to determine the cost/benefit of the two most common surgical surgical treatments for women - hysterectomy and myomectomy.
We provide an overview of some recent developments in machine learning tools for dynamic treatment regime discovery in precision medicine. The first development is a new off-policy reinforcement learning tool for continual learning in mobile health to enable patients with type 1 diabetes to exercise safely. The second development is a new inverse reinforcement learning tools which enables use of observational data to learn how clinicians balance competing priorities for treating depression and mania in patients with bipolar disorder. Both practical and technical challenges are discussed.
The method of differences-in-differences (DID) is widely used to estimate causal effects. The primary advantage of DID is that it can account for time-invariant bias from unobserved confounders. However, the standard DID estimator will be biased if there is an interaction between history in the after period and the groups. That is, bias will be present if an event besides the treatment occurs at the same time and affects the treated group in a differential fashion. We present a method of bounds based on DID that accounts for an unmeasured confounder that has a differential effect in the post-treatment time period. These DID bracketing bounds are simple to implement and only require partitioning the controls into two separate groups. We also develop two key extensions for DID bracketing bounds. First, we develop a new falsification test to probe the key assumption that is necessary for the bounds estimator to provide consistent estimates of the treatment effect. Next, we develop a method of sensitivity analysis that adjusts the bounds for possible bias based on differences between the treated and control units from the pretreatment period. We apply these DID bracketing bounds and the new methods we develop to an application on the effect of voter identification laws on turnout. Specifically, we focus estimating whether the enactment of voter identification laws in Georgia and Indiana had an effect on voter turnout.
We study experimental design in large-scale stochastic systems with substantial uncertainty and structured cross-unit interference. We consider the problem of a platform that seeks to optimize supply-side payments p in a centralized marketplace where different suppliers interact via their effects on the overall supply-demand equilibrium, and propose a class of local experimentation schemes that can be used to optimize these payments without perturbing the overall market equilibrium. We show that, as the system size grows, our scheme can estimate the gradient of the platform’s utility with respect to p while perturbing the overall market equilibrium by only a vanishingly small amount. We can then use these gradient estimates to optimize p via any stochastic first-order optimization method. These results stem from the insight that, while the system involves a large number of interacting units, any interference can only be channeled through a small number of key statistics, and this structure allows us to accurately predict feedback effects that arise from global system changes using only information collected while remaining in equilibrium.
We discuss a general roadmap for generating causal inference based on observational studies used to general real world evidence. We review targeted minimum loss estimation (TMLE), which provides a general template for the construction of asymptotically efficient plug-in estimators of a target estimand for realistic (i.e, infinite dimensional) statistical models. TMLE is a two stage procedure that first involves using ensemble machine learning termed super-learning to estimate the relevant stochastic relations between the treatment, censoring, covariates and outcome of interest. The super-learner allows one to fully utilize all the advances in machine learning (in addition to more conventional parametric model based estimators) to build a single most powerful ensemble machine learning algorithm. We present Highly Adaptive Lasso as an important machine learning algorithm to include.
In the second step, the TMLE involves maximizing a parametric likelihood along a so-called least favorable parametric model through the super-learner fit of the relevant stochastic relations in the observed data. This second step bridges the state of the art in machine learning to estimators of target estimands for which statistical inference is available (i.e, confidence intervals, p-values etc). We also review recent advances in collaborative TMLE in which the fit of the treatment and censoring mechanism is tailored w.r.t. performance of TMLE. We also discuss asymptotically valid bootstrap based inference. Simulations and data analyses are provided as demonstrations.
We describe different approaches for specifying models and prior distributions for estimating heterogeneous treatment effects using Bayesian nonparametric models. We make an affirmative case for direct, informative (or partially informative) prior distributions on heterogeneous treatment effects, especially when treatment effect size and treatment effect variation is small relative to other sources of variability. We also consider how to provide scientifically meaningful summaries of complicated, high-dimensional posterior distributions over heterogeneous treatment effects with appropriate measures of uncertainty.
Climate change mitigation has traditionally been analyzed as some version of a public goods game (PGG) in which a group is most successful if everybody contributes, but players are best off individually by not contributing anything (i.e., “free-riding”)—thereby creating a social dilemma. Analysis of climate change using the PGG and its variants has helped explain why global cooperation on GHG reductions is so difficult, as nations have an incentive to free-ride on the reductions of others. Rather than inspire collective action, it seems that the lack of progress in addressing the climate crisis is driving the search for a “quick fix” technological solution that circumvents the need for cooperation.
This seminar discussed ways in which to produce professional academic writing, from academic papers to research proposals or technical writing in general.
Machine learning (including deep and reinforcement learning) and blockchain are two of the most noticeable technologies in recent years. The first one is the foundation of artificial intelligence and big data, and the second one has significantly disrupted the financial industry. Both technologies are data-driven, and thus there are rapidly growing interests in integrating them for more secure and efficient data sharing and analysis. In this paper, we review the research on combining blockchain and machine learning technologies and demonstrate that they can collaborate efficiently and effectively. In the end, we point out some future directions and expect more researches on deeper integration of the two promising technologies.
In this talk, we discuss QuTrack, a Blockchain-based approach to track experiment and model changes primarily for AI and ML models. In addition, we discuss how change analytics can be used for process improvement and to enhance the model development and deployment processes.
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The Roman Empire A Historical Colossus.pdfkaushalkr1407
The Roman Empire, a vast and enduring power, stands as one of history's most remarkable civilizations, leaving an indelible imprint on the world. It emerged from the Roman Republic, transitioning into an imperial powerhouse under the leadership of Augustus Caesar in 27 BCE. This transformation marked the beginning of an era defined by unprecedented territorial expansion, architectural marvels, and profound cultural influence.
The empire's roots lie in the city of Rome, founded, according to legend, by Romulus in 753 BCE. Over centuries, Rome evolved from a small settlement to a formidable republic, characterized by a complex political system with elected officials and checks on power. However, internal strife, class conflicts, and military ambitions paved the way for the end of the Republic. Julius Caesar’s dictatorship and subsequent assassination in 44 BCE created a power vacuum, leading to a civil war. Octavian, later Augustus, emerged victorious, heralding the Roman Empire’s birth.
Under Augustus, the empire experienced the Pax Romana, a 200-year period of relative peace and stability. Augustus reformed the military, established efficient administrative systems, and initiated grand construction projects. The empire's borders expanded, encompassing territories from Britain to Egypt and from Spain to the Euphrates. Roman legions, renowned for their discipline and engineering prowess, secured and maintained these vast territories, building roads, fortifications, and cities that facilitated control and integration.
The Roman Empire’s society was hierarchical, with a rigid class system. At the top were the patricians, wealthy elites who held significant political power. Below them were the plebeians, free citizens with limited political influence, and the vast numbers of slaves who formed the backbone of the economy. The family unit was central, governed by the paterfamilias, the male head who held absolute authority.
Culturally, the Romans were eclectic, absorbing and adapting elements from the civilizations they encountered, particularly the Greeks. Roman art, literature, and philosophy reflected this synthesis, creating a rich cultural tapestry. Latin, the Roman language, became the lingua franca of the Western world, influencing numerous modern languages.
Roman architecture and engineering achievements were monumental. They perfected the arch, vault, and dome, constructing enduring structures like the Colosseum, Pantheon, and aqueducts. These engineering marvels not only showcased Roman ingenuity but also served practical purposes, from public entertainment to water supply.
How to Make a Field invisible in Odoo 17Celine George
It is possible to hide or invisible some fields in odoo. Commonly using “invisible” attribute in the field definition to invisible the fields. This slide will show how to make a field invisible in odoo 17.
The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
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Ethnobotany and Ethnopharmacology:
Ethnobotany in herbal drug evaluation,
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Reverse Pharmacology.
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
Program on Quasi-Monte Carlo and High-Dimensional Sampling Methods for Applied Mathematics Opening Workshop, QMC and Thinning for Empirical Datasets - Mike Giles, Sep 1, 2017
1. QMC and thinning for empirical datasets
Mike Giles, Fei Xie
Mathematical Institute, University of Oxford
SAMSI Opening Workshop on QMC and High-Dimensional
Sampling Methods for Applied Mathematics
August 28 – Sept 1, 2017
Mike Giles (Oxford) QMC & thinning August 28 – Sept 1, 2017 1 / 26
2. MLMC Motivation
In a MLMC nested expectation application (EVPPI), a new challenge
is that some of the random inputs come from a Bayesian posterior
distribution, with samples generated by MCMC methods.
x1 ∼ N(0, 1), x2 ∼ N(0, 1)
MCMC sampler: (y1, y2)
MCMC sampler: (z1, z2)
−→
−→
−→
fd (X, Y , Z) −→ fd
The standard procedure (?) would be to generate MCMC samples on
demand, and then use them immediately.
But how can this be extended to MLMC?
Mike Giles (Oxford) QMC & thinning August 28 – Sept 1, 2017 2 / 26
3. MCMC within MLMC
An alternative idea is to first pre-generate and store a large collection of
MCMC samples:
{Y1, Y2, Y3, . . . , YN}
Then, random samples for Y can be obtained from this collection by
uniformly distributed selection.
This avoids the problem of strong correlation between successive MCMC
samples, and also works fine for the MLMC calculation.
This approach also leads to ideas on QMC for empirical datasets, and
dataset thinning, reducing the number of samples which are stored.
Mike Giles (Oxford) QMC & thinning August 28 – Sept 1, 2017 3 / 26
4. Motivation
QMC methods generate quasi-uniformly distributed points in [0, 1]d ,
then map them to variables in some target domain X.
Requires a mapping f : [0, 1]d → X giving the desired distribution on X.
Question 1: what do we do if we want a uniform distribution on a large
empirical dataset X = {X0, X1, . . . , XN−1} ?
This question arose in a medical statistics application with some random
inputs coming from a Bayesian posterior distribution generated by MCMC.
Mike Giles (Oxford) QMC & thinning August 28 – Sept 1, 2017 4 / 26
5. Motivation
Our aim is to end up with the following mappings when N = 2dk
for some integer k:
[0, 1]d
−→ {0, 1, . . . 2k
−1}d
−→ {0, 1, . . . 2dk
−1} −→ X
first step is simply U → ⌊2k U⌋ applied to each dimension
other two steps are 1–1 giving uniform distribution on X
Question 2: if N is very large, can we thin it down to a subset of size
M ≪ N without introducing much error?
e.g. for 3D dataset, maybe generate 23×8 ≈ 16M samples, then thin it
down to perhaps 23×6 ≈ 260k samples
Mike Giles (Oxford) QMC & thinning August 28 – Sept 1, 2017 5 / 26
6. Recursive bisection
A simple idea: apply recursive bisection in alternating directions.
Step 1: sort points based on first coordinate to bisect into two halves
-4 -2 0 2 4
-4
-3
-2
-1
0
1
2
3
4
Mike Giles (Oxford) QMC & thinning August 28 – Sept 1, 2017 6 / 26
7. Recursive bisection
Step 2: within each half, sort points based on second coordinate to again
bisect
-4 -2 0 2 4
-4
-3
-2
-1
0
1
2
3
4
Mike Giles (Oxford) QMC & thinning August 28 – Sept 1, 2017 7 / 26
8. Recursive bisection
Step 3: in 2D, within each quarter, sort points based on first coordinate
-4 -2 0 2 4
-4
-3
-2
-1
0
1
2
3
4
Mike Giles (Oxford) QMC & thinning August 28 – Sept 1, 2017 8 / 26
9. Recursive bisection
Repeat until there is just one point in each subset
-4 -2 0 2 4
-4
-3
-2
-1
0
1
2
3
4
Mike Giles (Oxford) QMC & thinning August 28 – Sept 1, 2017 9 / 26
10. Recursive bisection
In this 2D example, we end up with 2k × 2k subsets (with one point each)
with the single entry for subset (i1, i2) stored at index I formed by
interleaving the bits:
i1 = 1 0 1
i2 = 0 1 0
=⇒ I = 1 0 0 1 1 0
This gives us our mapping: {0, 1, . . . 2k −1}2 −→ {0, 1, . . . 22d −1}
for the re-ordered data, but it is convenient to do a further re-ordering
so that we can access the same data through the simpler mapping
(i1, i2) −→ i1 + 2k i2
This all generalises naturally to higher dimensions.
Mike Giles (Oxford) QMC & thinning August 28 – Sept 1, 2017 10 / 26
11. QMC tests
We hope that this new approach applied to a dataset generated from
a known distribution is comparable to applying standard QMC to the
same distribution.
To test this, we consider the following 4 distributions:
2D independent Gaussians: X1 ∼ N(0, 1), X2 ∼ N(0, 1),
2D multivariate Gaussian: (X1, X2) ∼ N 0,
1 0
1/
√
2 1
2D distorted Gaussian: X1 = Z1, X2 = Z2+Z2
1 where
Z1 ∼ N(0, 1), Z2 ∼ N(0, 1),
double well distribution: X1 = Z1, X2 = Z2 where
U1 ∼ U(0, 1), Z2 ∼ N(0, 1),
and in each case use the test function f (x1, x2) = sin(x1+x2).
Mike Giles (Oxford) QMC & thinning August 28 – Sept 1, 2017 11 / 26
12. QMC tests
In each case, we
generate multiple random datasets of 220 points
for each, generate randomised QMC estimates using varying
numbers of Sobol points with digital scrambling
compare overall RMS error from 256 randomisations (of datasets and
scramblings) to that given by standard QMC and MC applied to same
distributions
Mike Giles (Oxford) QMC & thinning August 28 – Sept 1, 2017 12 / 26
13. QMC tests
Results for independent Gaussians
10 0
10 1
10 2
10 3
10 4
10 5
Number of Samples
10 -5
10 -4
10 -3
10 -2
10 -1
10 0
RootMeanSquaredError Independent Normals
std MC
std QMC
new
selection
Mike Giles (Oxford) QMC & thinning August 28 – Sept 1, 2017 13 / 26
14. QMC tests
Results for multivariate Gaussian
10 0
10 1
10 2
10 3
10 4
10 5
Number of Samples
10 -5
10 -4
10 -3
10 -2
10 -1
10 0
RootMeanSquaredError Correlated Normals
std MC
QMC+PCA
new
selection
Mike Giles (Oxford) QMC & thinning August 28 – Sept 1, 2017 14 / 26
15. QMC tests
Results for distorted Gaussian
10 0
10 1
10 2
10 3
10 4
10 5
Number of Samples
10 -4
10 -3
10 -2
10 -1
10 0
RootMeanSquaredError
Distorted Gaussian
std MC
std QMC
new
selection
Mike Giles (Oxford) QMC & thinning August 28 – Sept 1, 2017 15 / 26
16. QMC tests
Results for double well:
10 0
10 1
10 2
10 3
10 4
10 5
Number of Samples
10 -4
10 -3
10 -2
10 -1
10 0
RootMeanSquaredError
Double well
std MC
std QMC
new
selection
Mike Giles (Oxford) QMC & thinning August 28 – Sept 1, 2017 16 / 26
17. Thinning
The thinning idea builds on the same recursive bisection:
start with N = 2dk1 samples
recursively bisect into M = 2dk2 subsets, with k2 < k1
from each subset Sk, randomly select one point Y (j)
Note that the expectation over all random selections is unbiased, i.e.
E
1
M
j
f (Y (j)
)
=
1
M
j
M
N
X(j)∈Sk
f (X(j)
)
=
1
N
j
f (X(j)
)
Surprisingly (?), this is better in general (particularly in higher dimensions)
than using the mean of each subset, which gives a biased estimator.
Mike Giles (Oxford) QMC & thinning August 28 – Sept 1, 2017 17 / 26
18. Thinning
We are able to do some error analysis if we make the following assumption:
Assumption
The recursive bisection results in subsets Sk, k = 1, . . . , M in which
max
k
max
X(i),X(j)∈Sk
X(i)
− X(j)
= O(M−1/d
).
This seems reasonable, at least for distributions on bounded domains with
strictly positive densities, since there are O(M1/d ) blocks in each direction.
Mike Giles (Oxford) QMC & thinning August 28 – Sept 1, 2017 18 / 26
19. Thinning
Theorem
If the Assumption is satisfied, and the function f (x) is Lipschitz, with
|f (x)−f (y)| ≤ x−y , then the RMS thinning error is O(M−1/2−1/d ).
Proof: the error in subset Sk,
ek ≡ f (Y (k)
) −
M
N
X(j)∈Sk
f (X(j)
)
is zero mean, and because of the Assumption and the Lipschitz property
we have |ek | ≤ c M−1/d .
Hence, due to independence, the variance of the overall error is
V M−1
k
ek = M−2
k
V[ek ] ≤ M−1
c2
M−2/d
.
Mike Giles (Oxford) QMC & thinning August 28 – Sept 1, 2017 19 / 26
20. Thinning
A planar cut through a regular d-dimensional grid of blocks with N1/d in
each direction cuts at most d N(d−1)/d of the blocks. This inspires the
following theorem which considers functions with discontinuities.
Theorem
If the Assumption is satisfied, and the function f (x) is such that
|f (x)−f (y)| ≤ 1 for pairs (x, y) in O(N(d−1)/d ) of the subsets
|f (x)−f (y)| ≤ x−y for pairs (x, y) in the other subsets
then the RMS thinning error is O(M−1/2−1/2d ).
Proof: very similar to the previous one, with the dominant error
contribution from the first category of subsets.
Mike Giles (Oxford) QMC & thinning August 28 – Sept 1, 2017 20 / 26
21. Thinning
In both cases, the thinning error should be compared to the O(N−1/2)
sampling error arising from the original sample set being a collection of iid
samples from the true distribution.
Equating the two errors gives
M =
O(Nd/(d+2)), Lipschitz case
O(Nd/(d+1)), discontinuous case
This indicates the potential for significant savings in the size of M when
the dimension is low, but not so much in higher dimensions.
Mike Giles (Oxford) QMC & thinning August 28 – Sept 1, 2017 21 / 26
25. Thinning tests
10 1
10 2
10 3
10 4
10 5
10 6
Number of Samples
10 -5
10 -4
10 -3
10 -2
10 -1
10 0
RootMeanSquaredError
Double well
original samples
thinning method 1
thinning method 2
Mike Giles (Oxford) QMC & thinning August 28 – Sept 1, 2017 25 / 26
26. Conclusions
it seems the QMC approach is effective, at least in low dimensions,
so maybe a good way to apply QMC to samples generated by MCMC
the thinning also seems effective, particularly in low dimensions,
with its effectiveness depending on the smoothness of the integrand
Webpage: http://people.maths.ox.ac.uk/gilesm/slides.html
Email: mike.giles@maths.ox.ac.uk
Mike Giles (Oxford) QMC & thinning August 28 – Sept 1, 2017 26 / 26