nils-herrmann/clean_scirepeval huggingface dataset:
THE POLLS-A REVIEW PREELECTION SURVEY METHODOLOGY: DETAILS FROM EIGHT POLLING ORGANIZATIONS, 1988 AND 1992

D. STEPHEN voss is a doctoral candidate in the Department of Government of Harvard University. ANDREW GELMAN is assistant professor in the Department of Statistics in the University of California, Berkeley. GARY KING is professor in the Department of Government of Harvard University. They thank the following individuals for their openness and detailed assistance: David K. Krane and Robert Spanski of Louis Harris and Associates; Martin Frankel, a consultant for Louis Harris and statistian at Baruch College; Kathleen Frankovic, Bala Ramnath, and Marla Kaye of CBS; Sharon Warden of the Washington Post; Kristen Conrad of Chilton Research; Dale Kulp and Amy Starer of Marketing Systems Group, responsible for the GENESYS Sampling System; Steve Shaw of Media General; Kathy Walenczyk and Linda Piekarski of Survey Sampling, Inc.; Dan Soulas of ICR Survey Research Group; Bums W. Roper, Peter Case, and Brad Fay of the Roper Organization; Hal Quinley of Yankelovich; Jeff Alderman of ABC; Shari Weber of Gallup; and especially Gallup's Jack Ludwig, who weathered the inherent inefficiency of a trial run. They also thank Mark Lew and Cassie Hartzog for programming assistance; Bradley Palmquist, Dale Kulp, Roger Purves, Bob Groves, and anonymous reviewers for comments and corrections; and the National Science Foundation for grants SBR-9223637 (to Gelman and King), DMS-9457824 (to Gelman), and SBR-932121 (to King). Gary King also thanks the John Simon Guggenheim Memorial Foundation for a fellowship during which time this research was completed. E-mail addresses are Voss, dsvoss@isr.harvard.edu; Gelman, gelman@stat.berkeley.edu; King, gking@harvard.edu. 1. Most of these data are available through the Inter-University Consortium for Political and Social Research (ICPSR) or the Roper Center for Public Opinion Research.
Bayes Factors 1

In a 1935 paper, and in his book Theory of Probability, Je reys developed a methodology for quantifying the evidence in favor of a scienti c theory. The centerpiece was a number, now called the Bayes factor, which is the posterior odds of the null hypothesis when the prior probability on the null is one-half. Although there has been much discussion of Bayesian hypothesis testing in the context of criticism of P -values, less attention has been given to the Bayes factor as a practical tool of applied statistics. In this paper we review and discuss the uses of Bayes factors in the context of ve scienti c applications in genetics, sports, ecology, sociology and psychology. The points we emphasize are: | from Je reys' Bayesian point of view, the purpose of hypothesis testing is to evaluate the evidence in favor of a scienti c theory; | Bayes factors o er a way of evaluating evidence in favor of a null hypothesis; | Bayes factors provide a way of incorporating external information into the evaluation of evidence about a hypothesis; | Bayes factors are very general, and do not require alternative models to be nested; | several techniques are available for computing Bayes factors, including asymptotic approximations which are easy to compute using the output from standard packages that maximize likelihoods; | in \non-standard" statistical models that do not satisfy common regularity conditions, it can be technically simpler to calculate Bayes factors than to derive non-Bayesian signi cance tests; | the Schwarz criterion (or BIC) gives a crude approximation to the logarithm of the Bayes factor, which is easy to use and does not require evaluation of prior distributions; | when one is interested in estimation or prediction, Bayes factors may be converted to weights to be attached to various models so that a composite estimate or prediction may be obtained that takes account of structural or model uncertainty; | algorithms have been proposed that allow model uncertainty to be taken into account when the class of models initially considered is very large; | Bayes factors are useful for guiding an evolutionary model-building process; | and, nally, it is important, and feasible, to assess the sensitivity of conclusions to the prior distributions used.
Avoiding Model Selection in Bayesian Social Research

Raftery's paper addresses two important problems in the statistical analysis of social science data: (1) choosing an appropriate model when so much data are available that standard P-values reject all parsimonious models; and (2) making estimates and predictions when there are not enough data available to fit the desired model using standard techniques. For both problems, we agree with Raftery that classical frequentist methods fail and that Raftery's suggested methods based on BIC can point in better directions. Nevertheless, we disagree with his solutions because, in principle, they are still directed off-target and only by serendipity manage to hit the target in special circumstances. Our primary criticisms of Raftery's proposals are that (1) he promises the impossible: the selection of a model that is adequate for specific purposes without consideration of those purposes; and (2) he uses the same limited tool for model averaging as for model selection, thereby depriving himself of the benefits of the broad range of available Bayesian procedures. Despite our criticisms, we applaud Raftery's desire to improve practice by providing methods and computer programs for all to use and applying these methods to real problems. We believe that his paper makes a positive contribution to social science, by focusing on
Bayesian Data Analysis

FUNDAMENTALS OF BAYESIAN INFERENCE Probability and Inference Single-Parameter Models Introduction to Multiparameter Models Asymptotics and Connections to Non-Bayesian Approaches Hierarchical Models FUNDAMENTALS OF BAYESIAN DATA ANALYSIS Model Checking Evaluating, Comparing, and Expanding Models Modeling Accounting for Data Collection Decision Analysis ADVANCED COMPUTATION Introduction to Bayesian Computation Basics of Markov Chain Simulation Computationally Efficient Markov Chain Simulation Modal and Distributional Approximations REGRESSION MODELS Introduction to Regression Models Hierarchical Linear Models Generalized Linear Models Models for Robust Inference Models for Missing Data NONLINEAR AND NONPARAMETRIC MODELS Parametric Nonlinear Models Basic Function Models Gaussian Process Models Finite Mixture Models Dirichlet Process Models APPENDICES A: Standard Probability Distributions B: Outline of Proofs of Asymptotic Theorems C: Computation in R and Stan Bibliographic Notes and Exercises appear at the end of each chapter.
Method of Moments Using Monte Carlo Simulation

We present a computational approach to the method of moments using Monte Carlo simulation. Simple algebraic identities are used so that all computations can be performed directly using simulation draws and computation of the derivative of the log-likelihood. We present a simple implementation using the Newton-Raphson algorithm with the understanding that other optimization methods may be used in more complicated problems. The method can be applied to families of distributions with unknown normalizing constants and can be extended to least squares fitting in the case that the number of moments observed exceeds the number of parameters in the model. The method can be further generalized to allow “moments” that are any function of data and parameters, including as a special case maximum likelihood for models with unknown normalizing constants or missing data. In addition to being used for estimation, our method may be useful for setting the parameters of a Bayes prior distribution by specifying mome...
Learning Planning Rules in Noisy Stochastic Worlds

We present an algorithm for learning a model of the effects of actions in noisy stochastic worlds. We consider learning in a 3D simulated blocks world with realistic physics. To model this world, we develop a planning representation with explicit mechanisms for expressing object reference and noise. We then present a learning algorithm that can create rules while also learning derived predicates, and evaluate this algorithm in the blocks world simulator, demonstrating that we can learn rules that effectively model the world dynamics.
Learning and Planning with Probabilistic Relational Rules

A three-dimensional blocks-world simulation built with the OpenDynamics toolkit [7]. The world consists of a table, blocks of roughly uniform size and mass, and a robotic hand that is moved by simulated motors.  Robust robotic control in complex worlds is a challenging problem. Hand-engineering a solution is difficult and time-consuming. Developing techniques that will allow robots to gather knowledge about the world and use it to design their own control strategies seems like a reasonable alternative.  We represent world action dynamics using probabilistic planning rules. Figure refrelrules-fig shows two rules that model actions that can be performed by the robotic arm in the blocks world of Figure 1. Such rules enable us to take advantage of the inherent structure found in many uncertain, complex environments by making the following assumptions about the world:
Decayed MCMC iltering

Filtering--estimating the state of a partially observable Markov process from a sequence of observations-is one of the most widely studied problems in control theory, AI, and computational statistics. Exact computation of the posterior distribution is generally intractable for large discrete systems and for nonlinear continuous systems, so a good deal of effort has gone into developing robust approximation algorithms. This paper describes a simple stochastic approximation algorithm for filtering called decayed MCMC. The algorithm applies Markov chain Monte Carlo sampling to the space of state trajectories using a proposal distribution that favours flips of more recent state variables. The formal analysis of the algorithm involves a generalization of standard coupling arguments for MCMC convergence. We prove that for any ergodic underlying Markov process, the convergence time of decayed MCMC with inversepolynomial decay remains bounded as the length of the observation sequence grows. We show experimentally that decayed MCMC is at least competitive with other approximation algorithms such as particle filtering.
Statistical Visual Language Models for Ink Parsing

In this paper we motivate a new technique for automatic recognition of hand-sketched digital ink. By viewing sketched drawings as utterances in a visual language, sketch recognition can be posed as an ambiguous parsing problem. On this premise we have developed an algorithm for ink parsing that uses a statistical model to disambiguate. Under this formulation, writing a new recognizer for a visual language is as simple as writing a declarative grammar for the language, generating a model from the grammar, and training the model on drawing examples. We evaluate the speed and accuracy of this approach for the sample domain of the SILK visual language and report positive initial results.
Approximate inference for first-order probabilistic languages

A new, general approach is described for approximate inference in first-order probabilistic languages, using Markov chain Monte Carlo (MCMC) techniques in the space of concrete possible worlds underlying any given knowledge base. The simplicity of the approach and its lazy construction of possible worlds make it possible to consider quite expressive languages. In particular, we consider two extensions to the basic relational probability models (RPMs) defined by Koller and Pfeffer, both of which have caused difficulties for exact algorithms. The first extension deals with uncertainty about relations among objects, where MCMC samples over relational structures. The second extension deals with uncertainty about the identity of individuals, where MCMC samples over sets of equivalence classes of objects. In both cases, we identify types of probability distributions that allow local decomposition of inference while encoding possible domains in a plausible way. We apply our algorithms to simple examples and show that the MCMC approach scales well.
A Bayesian approach to object identification

There are many real world domains where an agent can observe the world state only partially and intermittently, using noisy sensors. Merely keeping track of the objects present in such a system is non-trivial. The problem may be complicated further if the system dynamics are not fully known or unpredictable, so that some on-line learning is necessary. I have been working on a principled approach to state estinaation and prediction under these realistic conditions. So far, I have focused mostly on object identification, deciding if some newly observed object is the same as a previously observed one. The work has been applied to the surveillance of a large metropolitan freeway system. The vehicles are observed through scattered cameras, and both the viewing and traffic conditions are highly variable. The general approach is based on a probabilistic formulation of the domain. The conditional independence and variable density distribution assumptions are made explicit, so the domain can be expressed as a dynamic probabilistic network (DPN). The right assumptions can simplify the problem considerably, although care must be taken to make them realistic. As an example, consider the traffic surveillance domain. There, we can assume that certain observations are dependent on the sensor where they are made, and the object generating them, but not directly dependent on what happens at other sensors. Color, for example, is constant for each vehicle, and the way it appears at each observation site depends on the characteristics of that site. The introduction of hidden variables representing such features of objects in the system enables us to remove many intersensor dependencies. As a result, we can use small, local probability models. We learn model parameters using the Expectation Maximazation (EM) algorithm. Unfortunately, inference in large, complex problems will remain intractable even if the probabilistic network is simplified. The correspondence between observations and objects is unknown, and so inference in the system should involve summing over all the possibilities. Since the number of possible observation-object assignments is exponential in the number of objects, solving such a summation exactly is a #P problem. Our solu-
Tracking Many Objects with Many Sensors

Keeping track of multiple objects over time is a problem that arises in many real-world domains. The problem is often complicated by noisy sensors and unpredictable dynamics. Previous work by Huang and Russell, drawing on the data association literature, provided a probabilistic analysis and a threshold-based approximation algorithm for the case of multiple objects detected by two spatially separated sensors. This paper analyses the case in which large numbers of sensors are involved. We show that the approach taken by Huang and Russell, who used pairwise sensor-based appearance probabilities as the elementary probabilistic model, does not scale. When more than two observations are made, the objects' intrinsic properties must be estimated. These provide the necessary conditional independencies to allow a spatial decomposition of the global probability model. We also replace Huang and Russell's threshold algorithm for object identification with a polynomial-time approximation scheme based on Markov chain Monte Carlo simulation. Using sensor data from a freeway traffic simulation, we show that this allows accurate estimation of long-range origin/destination information even when the individual links in the sensor chain are highly unreliable.
Dynamic model of the octopus arm. I. Biomechanics of the octopus reaching movement.

The octopus arm requires special motor control schemes because it consists almost entirely of muscles and lacks a rigid skeletal support. Here we present a 2D dynamic model of the octopus arm to explore possible strategies of movement control in this muscular hydrostat. The arm is modeled as a multisegment structure, each segment containing longitudinal and transverse muscles and maintaining a constant volume, a prominent feature of muscular hydrostats. The input to the model is the degree of activation of each of its muscles. The model includes the external forces of gravity, buoyancy, and water drag forces (experimentally estimated here). It also includes the internal forces generated by the arm muscles and the forces responsible for maintaining a constant volume. Using this dynamic model to investigate the octopus reaching movement and to explore the mechanisms of bend propagation that characterize this movement, we found the following. 1) A simple command producing a wave of muscle activation moving at a constant velocity is sufficient to replicate the natural reaching movements with similar kinematic features. 2) The biomechanical mechanism that produces the reaching movement is a stiffening wave of muscle contraction that pushes a bend forward along the arm. 3) The perpendicular drag coefficient for an octopus arm is nearly 50 times larger than the tangential drag coefficient. During a reaching movement, only a small portion of the arm is oriented perpendicular to the direction of movement, thus minimizing the drag force.
Learning Wireless Network Association-Control with Gaussian Process Temporal Difference Methods

This paper deals with the problem of improving the performance of wireless networks through the use of association control, which is the activity of intelligently associating users with the network's access points (APs), taking advantage of overlaps in the coverage areas of the APs. The optimal solution to this problem is classified as NP-hard. We present an innovative association control method which utilizes a novel Reinforcement Learning (RL) algorithm – Gaussian Processes Temporal Differences (GPTD). GPTD, an algorithm which addresses the value function estimation in continuous state spaces, and GPSARSA, an algorithm which uses GPTD to compute a complete RL solution, were defined and presented by Engel et al GPTD has only been tested so far on simple and theoretical problems, and there was a desire to test its behavior under the conditions of real-life problems. In this study we attempt to accomplish these two symbiotic goals of (i) proposing a solution to the association control problem and also (ii) developing a realistic testing environment under OPNET for GPTD and for RL in general.
Learning to Control an Octopus Arm with Gaussian Process Temporal Difference Methods

The Octopus arm is a highly versatile and complex limb. How the Octopus controls such a hyper-redundant arm (not to mention eight of them!) is as yet unknown. Robotic arms based on the same mechanical principles may render present day robotic arms obsolete. In this paper, we tackle this control problem using an online reinforcement learning algorithm, based on a Bayesian approach to policy evaluation known as Gaussian process temporal difference (GPTD) learning. Our substitute for the real arm is a computer simulation of a 2-dimensional model of an Octopus arm. Even with the simplifications inherent to this model, the state space we face is a high-dimensional one. We apply a GPTD-based algorithm to this domain, and demonstrate its operation on several learning tasks of varying degrees of difficulty.
Algorithms and Representations for Reinforcement Learning

Machine Learning is a field of research aimed at constructing intelligent machines that gain and improve their skills by learning and adaptation. As such, Machine Learning research addresses several classes of learning problems, including for instance, supervised and unsupervised learning. Arguably, the most ubiquitous and realistic class of learning problems, faced by both living creatures and artificial agents, is known as Reinforcement Learning. Reinforcement Learning problems are characterized by a long-term interaction between the learning agent and a dynamic, unfamiliar, uncertain, possibly even hostile environment. Mathematically, this interaction is modeled as a Markov Decision Process (MDP). Probably the most significant contribution of this thesis is in the introduction of a new class of Reinforcement Learning algorithms, which leverage the power of a statistical set of tools known as Gaussian Processes. This new approach to Reinforcement Learning offers viable solutions to some of the major limitations of current Reinforcement Learning methods, such as the lack of confidence intervals for performance predictions, and the difficulty of appropriately reconciling exploration with exploitation. Analysis of these algorithms and their relationship with existing methods also provides us with new insights into the assumptions underlying some of the most popular Reinforcement Learning algorithms to date.
, Ranit Aharonov , Yaakov Engel , Binyamin of the Octopus Reaching Movement Dynamic Model of the Octopus Arm

, November 1, 2005; 208 (21): iv. J Exp Biol Laura Blackburn AGILE ANIMALS [PDF]  [ , October 1, 2006; 209 (19): 3697-3707.  Huffard between primary and secondary defenses (Cephalopoda: Octopodidae): walking the line Abdopus aculeatus Locomotion by [PDF]  [ , September 1, 2007; 98 (3): 1775-1790.  , December 1, 2007; 210 (23): 4069-4082.  Wedeen Anatomical basis of lingual hydrostatic deformation [PDF]  [ , November , 2010; 109 (5): 1500-1514.  Milos Kojic, Alvin Liang, Van J. Wedeen and mechanics of mesoscale myofiber tracts obtained by MRI Derivation of a finite-element model of lingual deformation during swallowing from the
Kernel Recursive Least Squares

We present a non-linear kernel-based version of the Recursive Least Squares (RLS) algorithm. Our Kernel-RLS algorithm performs linear regression in the feature space induced by a Mercer kernel, and can therefore be used to recursively construct the minimum meansquared-error regressor. Sparsity (and therefore regularization) of the solution is achieved by an explicit greedy sparsification process that admits into the kernel representation a new input sample only if its feature space image is linearly independent of the images of previously admitted samples. Most importantly, this sparsification procedure allows the algorithm to operate online. We demonstrate the performance and scaling properties of the Kernel-RLS algorithm as compared to a state-of-the-art Support Vector Regression algorithm, on both synthetic and real data.
Lecture 19: Leave-one-out approximations

We introduce the idea of cross-validation, leave-one-out in its extreme form. We show that the leave-one-out estimate is almost unbiased. We then show a series of approximations and bounds on the leave-one-out error that are used for computational efficiency. First this is shown for least-squares loss then for the SVM loss function. We close by reporting in a worst case analysis the leave-one-out error is not a significantly better estimate of expected error than is the training error.
Feature reduction and hierarchy of classifiers for fast object detection in video images

We present a two-step method to speed-up object detection systems in computer vision that use Support Vector Machines (SVMs) as classifiers. In a first step we perform feature reduction by choosing relevant image features according to a measure derived from statistical learning theory. In a second step we build a hierarchy of classifiers. On the bottom level, a simple and fast classifier analyzes the whole image and rejects large parts of the background On the top level, a slower but more accurate classifier performs the final detection. Experiments with a face detection system show that combining feature reduction with hierarchical classification leads to a speed-up by a factor of 170 with similar classification performance.