subject:(exact inference)

Cornell University

1. Wang, Ke Alexander. Large Scale Exact Gaussian Processes Inference and Euclidean Constrained Neural Networks with Physics Priors.

Degree: M.S., Computer Science, Computer Science, 2020, Cornell University

URL: http://hdl.handle.net/1813/70285

► Intelligent systems that interact with the physical world must be able to model the underlying dynamics accurately to be able to make informed actions and… (more)

Subjects/Keywords: exact inference; Gaussian process; hamiltonian; lagrangian; neural networks; physics priors

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APA (6^th Edition):

Wang, K. A. (2020). Large Scale Exact Gaussian Processes Inference and Euclidean Constrained Neural Networks with Physics Priors. (Masters Thesis). Cornell University. Retrieved from http://hdl.handle.net/1813/70285

Chicago Manual of Style (16^th Edition):

Wang, Ke Alexander. “Large Scale Exact Gaussian Processes Inference and Euclidean Constrained Neural Networks with Physics Priors.” 2020. Masters Thesis, Cornell University. Accessed April 22, 2021. http://hdl.handle.net/1813/70285.

MLA Handbook (7^th Edition):

Wang, Ke Alexander. “Large Scale Exact Gaussian Processes Inference and Euclidean Constrained Neural Networks with Physics Priors.” 2020. Web. 22 Apr 2021.

Vancouver:

Wang KA. Large Scale Exact Gaussian Processes Inference and Euclidean Constrained Neural Networks with Physics Priors. [Internet] [Masters thesis]. Cornell University; 2020. [cited 2021 Apr 22]. Available from: http://hdl.handle.net/1813/70285.

Council of Science Editors:

Wang KA. Large Scale Exact Gaussian Processes Inference and Euclidean Constrained Neural Networks with Physics Priors. [Masters Thesis]. Cornell University; 2020. Available from: http://hdl.handle.net/1813/70285

University of Wolverhampton

2. Aziz, Wilker Ferreira. Exact sampling and optimisation in statistical machine translation.

Degree: PhD, 2014, University of Wolverhampton

URL: http://hdl.handle.net/2436/314591

► In Statistical Machine Translation (SMT), inference needs to be performed over a high-complexity discrete distribution de ned by the intersection between a translation hypergraph and… (more)

▼ In Statistical Machine Translation (SMT), inference needs to be performed over a high-complexity discrete distribution de ned by the intersection between a translation hypergraph and a target language model. This distribution is too complex to be represented exactly and one typically resorts to approximation techniques either to perform optimisation { the task of searching for the optimum translation { or sampling { the task of nding a subset of translations that is statistically representative of the goal distribution. Beam-search is an example of an approximate optimisation technique, where maximisation is performed over a heuristically pruned representation of the goal distribution. For inference tasks other than optimisation, rather than nding a single optimum, one is really interested in obtaining a set of probabilistic samples from the distribution. This is the case in training where one wishes to obtain unbiased estimates of expectations in order to t the parameters of a model. Samples are also necessary in consensus decoding where one chooses from a sample of likely translations the one that minimises a loss function. Due to the additional computational challenges posed by sampling, n-best lists, a by-product of optimisation, are typically used as a biased approximation to true probabilistic samples. A more direct procedure is to attempt to directly draw samples from the underlying distribution rather than rely on n-best list approximations. Markov Chain Monte Carlo (MCMC) methods, such as Gibbs sampling, o er a way to overcome the tractability issues in sampling, however their convergence properties are hard to assess. That is, it is di cult to know when, if ever, an MCMC sampler is producing samples that are compatible iii with the goal distribution. Rejection sampling, a Monte Carlo (MC) method, is more fundamental and natural, it o ers strong guarantees, such as unbiased samples, but is typically hard to design for distributions of the kind addressed in SMT, rendering an intractable method. A recent technique that stresses a uni ed view between the two types of inference tasks discussed here | optimisation and sampling | is the OS approach. OS can be seen as a cross between Adaptive Rejection Sampling (an MC method) and A optimisation. In this view the intractable goal distribution is upperbounded by a simpler (thus tractable) proxy distribution, which is then incrementally re ned to be closer to the goal until the maximum is found, or until the sampling performance exceeds a certain level. This thesis introduces an approach to exact optimisation and exact sampling in SMT by addressing the tractability issues associated with the intersection between the translation hypergraph and the language model. The two forms of inference are handled in a uni ed framework based on the OS approach. In short, an intractable goal distribution, over which one wishes to perform inference, is upperbounded by tractable proposal distributions. A proposal represents a relaxed version of the complete space of weighted…

Subjects/Keywords: 418.020285635; statistical machine translation; exact inference; optimisation; sampling; coarse-to-fine search; decoding

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Aziz, W. F. (2014). Exact sampling and optimisation in statistical machine translation. (Doctoral Dissertation). University of Wolverhampton. Retrieved from http://hdl.handle.net/2436/314591

Chicago Manual of Style (16^th Edition):

Aziz, Wilker Ferreira. “Exact sampling and optimisation in statistical machine translation.” 2014. Doctoral Dissertation, University of Wolverhampton. Accessed April 22, 2021. http://hdl.handle.net/2436/314591.

MLA Handbook (7^th Edition):

Aziz, Wilker Ferreira. “Exact sampling and optimisation in statistical machine translation.” 2014. Web. 22 Apr 2021.

Vancouver:

Aziz WF. Exact sampling and optimisation in statistical machine translation. [Internet] [Doctoral dissertation]. University of Wolverhampton; 2014. [cited 2021 Apr 22]. Available from: http://hdl.handle.net/2436/314591.

Council of Science Editors:

Aziz WF. Exact sampling and optimisation in statistical machine translation. [Doctoral Dissertation]. University of Wolverhampton; 2014. Available from: http://hdl.handle.net/2436/314591

UCLA

3. Chen, Suming Jeremiah. Robust Decision Making with the Same-Decision Probability.

Degree: Computer Science, 2015, UCLA

URL: http://www.escholarship.org/uc/item/5qh0c61r

► When making decisions under uncertainty, the optimal choices are often difficult to discern, especially if not enough information has been gathered. Two key questions in… (more)

▼ When making decisions under uncertainty, the optimal choices are often difficult to discern, especially if not enough information has been gathered. Two key questions in this regard relate to whether one should stop the information gathering process and commit to a decision (stopping criterion), and if not, what information to gather next (selection criterion). The proposed thesis is concerned with addressing this problem in light of a new advance, known as the Same – Decision Probability (SDP), which is the probability that we would make the same decision had we known what we currently do not know. In this thesis, we show how the SDP can be used to be an effective stopping criterion, and compare it to traditional criteria to demonstrate how it provides a fresh perspective in decision making under uncertainty. Additionally, we develop the first exact algorithm to compute the SDP so that it may be used as a stopping criterion. We demonstrate the effectiveness of these algorithms on real and synthetic networks, and show that our proposed stopping criterion can lead to an early stopping of information gathering. Furthermore, we demonstrate that the SDP can be used as a selection criterion. In particular, since there are many criteria for measuring the value of information, each based on optimizing different objectives, we propose a new SDP-based criterion for measuring the value of information – this criterion values information that leads to robust decisions (i.e., ones that are unlikely to change due to new information). We develop the first algorithm to optimize the value of information, given the SDP as the reward criterion, and show empirical results that prove the utility of this novel criterion. We further answer several questions regarding the computational complexity of the SDP, which is known to be PP^PP-complete. Finally, we present results of applying the SDP as an information gathering criterion in practical problems including tutoring systems (do we need to ask more questions?) and machine learning (do we have enough data?).

Subjects/Keywords: Computer science; Artificial intelligence; automated reasoning; Bayesian networks; decision making; exact probabilistic inference; machine learning; robustness

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APA (6^th Edition):

Chen, S. J. (2015). Robust Decision Making with the Same-Decision Probability. (Thesis). UCLA. Retrieved from http://www.escholarship.org/uc/item/5qh0c61r

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Chen, Suming Jeremiah. “Robust Decision Making with the Same-Decision Probability.” 2015. Thesis, UCLA. Accessed April 22, 2021. http://www.escholarship.org/uc/item/5qh0c61r.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Chen, Suming Jeremiah. “Robust Decision Making with the Same-Decision Probability.” 2015. Web. 22 Apr 2021.

Vancouver:

Chen SJ. Robust Decision Making with the Same-Decision Probability. [Internet] [Thesis]. UCLA; 2015. [cited 2021 Apr 22]. Available from: http://www.escholarship.org/uc/item/5qh0c61r.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Chen SJ. Robust Decision Making with the Same-Decision Probability. [Thesis]. UCLA; 2015. Available from: http://www.escholarship.org/uc/item/5qh0c61r

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Colorado

4. Gaertner, Matthew Newman. Assessing a New Approach to Class-Based Affirmative Action.

Degree: PhD, Education, 2011, University of Colorado

URL: https://scholar.colorado.edu/educ_gradetds/11

► In November, 2008, Colorado and Nebraska voted on amendments that sought to end race-based affirmative action at public universities in those states. In anticipation… (more)

▼ In November, 2008, Colorado and Nebraska voted on amendments that sought to end race-based affirmative action at public universities in those states. In anticipation of the vote, the University of Colorado at Boulder (CU) explored statistical approaches to support class-based (i.e., socioeconomic) affirmative action. This dissertation introduces CU's method of identifying socioeconomically disadvantaged and overachieving applicants in undergraduate admissions. In addition, sensitivity analyses were conducted to gauge the impact of technical decisions that were made when these measures were devised. Two experiments were carried out to determine whether or not implementing this approach would change the racial and socioeconomic diversity of accepted classes. Finally, historical student records were examined to explore the likelihood of college success for the beneficiaries of CU's class-based approach. The sensitivity analyses identify particularly consequential issues that architects of class-based systems may face, including modeling application to college, defining target populations, and addressing missing data. The experiments suggest class-based affirmative action can potentially increase acceptance rates for low-SES and minority applicants, particularly if it is used alongside race-conscious admissions. Analyses of historical data do not rule out the possibility of college success for the beneficiaries of class-conscious admissions, but they do argue for the provision of robust academic support to marginally qualified, low-SES students when they matriculate. This dissertation is intended to serve as a resource for postsecondary institutions considering class-based admissions policies. If race-based approaches are overturned, universities like CU could struggle to develop race-blind metrics to identify applicants who have faced adversity. This research examines one method of quantifying the barriers these students encounter. Advisors/Committee Members: Derek Briggs, Edward Wiley, Michele Moses.

Subjects/Keywords: Affirmative Action; Causal Inference; Coarsened Exact Matching; College Access; Experimental Design; Social Class; Educational Assessment, Evaluation, and Research; Higher Education

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Gaertner, M. N. (2011). Assessing a New Approach to Class-Based Affirmative Action. (Doctoral Dissertation). University of Colorado. Retrieved from https://scholar.colorado.edu/educ_gradetds/11

Chicago Manual of Style (16^th Edition):

Gaertner, Matthew Newman. “Assessing a New Approach to Class-Based Affirmative Action.” 2011. Doctoral Dissertation, University of Colorado. Accessed April 22, 2021. https://scholar.colorado.edu/educ_gradetds/11.

MLA Handbook (7^th Edition):

Gaertner, Matthew Newman. “Assessing a New Approach to Class-Based Affirmative Action.” 2011. Web. 22 Apr 2021.

Vancouver:

Gaertner MN. Assessing a New Approach to Class-Based Affirmative Action. [Internet] [Doctoral dissertation]. University of Colorado; 2011. [cited 2021 Apr 22]. Available from: https://scholar.colorado.edu/educ_gradetds/11.

Council of Science Editors:

Gaertner MN. Assessing a New Approach to Class-Based Affirmative Action. [Doctoral Dissertation]. University of Colorado; 2011. Available from: https://scholar.colorado.edu/educ_gradetds/11

Brno University of Technology

5. Šimeček, Josef. Inference v Bayesovských sítích: Inference in Bayesian Networks.

Degree: 2018, Brno University of Technology

URL: http://hdl.handle.net/11012/53484

► This master's thesis deals with demonstration of various approaches to probabilistic inference in Bayesian networks. Basics of probability theory, introduction to Bayesian networks, methods for… (more)

Subjects/Keywords: Bayesovské sítě; pravděpodobnostní inference; exaktní inference; Kimův a Pearlův algoritmus posílání zpráv; transformace na rozložitelný model; logické vzorkování; věrohodnostní váhování; Gibbsovo vzorkování; Bayesian networks; probabilistic inference; exact inference; Kim and Pearl's message passing algorithm; junction tree algorithm; logic sampling; likelihood weighting; Gibbs sampling

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Šimeček, J. (2018). Inference v Bayesovských sítích: Inference in Bayesian Networks. (Thesis). Brno University of Technology. Retrieved from http://hdl.handle.net/11012/53484

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Šimeček, Josef. “Inference v Bayesovských sítích: Inference in Bayesian Networks.” 2018. Thesis, Brno University of Technology. Accessed April 22, 2021. http://hdl.handle.net/11012/53484.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Šimeček, Josef. “Inference v Bayesovských sítích: Inference in Bayesian Networks.” 2018. Web. 22 Apr 2021.

Vancouver:

Šimeček J. Inference v Bayesovských sítích: Inference in Bayesian Networks. [Internet] [Thesis]. Brno University of Technology; 2018. [cited 2021 Apr 22]. Available from: http://hdl.handle.net/11012/53484.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Šimeček J. Inference v Bayesovských sítích: Inference in Bayesian Networks. [Thesis]. Brno University of Technology; 2018. Available from: http://hdl.handle.net/11012/53484

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

6. Cortijo Aragon, Santiago José. Sécurité pour des infrastructures critiques SCADA fondée sur des modèles graphiques probabilistes : Probabilistic graphical model-based security for SCADA critical infrastructures.

Degree: Docteur es, Informatique, 2018, Sorbonne université

URL: http://www.theses.fr/2018SORUS502

►

Dans la présente thèse, deux nouveaux modèles basés sur les Réseaux Bayésiens (BN) sont proposés: les BN à densités conditionnelles tronquées (ctdBN) et les BN… (more)

▼

Dans la présente thèse, deux nouveaux modèles basés sur les Réseaux Bayésiens (BN) sont proposés: les BN à densités conditionnelles tronquées (ctdBN) et les BN à densités conditionnelles (cdBN). Ceux-ci permettent la modélisation de probabilités jointes pour des systèmes avec des variables aléatoires discrètes et continues. Nous analysons la complexité algorithmique pour l'inférence exacte dans les modèles proposés et montrons qu'elles sont du même ordre que celle des BNs classiques. Nous étudions également le problème d’apprentissage des cdBNs: nous proposons une fonction de score basée sur le score BD, ainsi qu’un algorithme d'apprentissage basé sur l'algorithme EM structural, tout en supposant l'existence de variables latentes discrètes correspondantes à chaque variable continue. En outre, nous prouvons théoriquement que les modèles cdBN et ctdBN peuvent approcher n'importe quelle distribution de probabilité jointe Lipschitzienne, montrant ainsi l'expressivité de ces modèles. Dans le cadre du projet Européen SCISSOR, dont le but est la cyber-securité, nous utilisons le modèle cdBN pour décrire la dynamique d'un système SCADA et diagnostiquer des anomalies dans des observations prises en temps réel, tout en interprétant une anomalie comme une menace potentielle à l'intégrité du système.

In this thesis two new Bayesian-Network-based models are proposed: conditional truncated densities Bayesian networks (ctdBN) and conditional densities Bayesian networks (cdBN). They model joint probability distributions of systems combining discrete and continuous random variables. We analyze the complexity of exact inference for the proposed models, concluding that they are in the same order of the one for the classical Bayesian Network model. We also analyze the challenge of learning cdBNs, proposing a score function based in the BD score as well as a whole learning algorithm based on the structural EM algorithm, assuming the existence of discrete latent variables corresponding to each continuous variable. In addition, we proof theoretically that the cdBN and ctdBN models can approximate well any Lipschitz joint probability distribution, which shows the expressiveness of these models. Within the framework of the European project SCISSOR, whose goal is cyber-security, we use the cdBN model to describe the dynamics of a SCADA system and to diagnose anomalies in observations taken in real time, interpreting an anomaly as a potential threat to the integrity of the system.

Advisors/Committee Members: Gonzales, Christophe (thesis director).

Subjects/Keywords: Réseaux bayésiens hybrides; Fonctions de score; Apprentissage; Inférence exacte; SCADA; Cyber-sécurité; Hybrid Bayesian networks; Score functions; Learning; Exact inference; SCADA; Digital security; 519.54; 006.3

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Cortijo Aragon, S. J. (2018). Sécurité pour des infrastructures critiques SCADA fondée sur des modèles graphiques probabilistes : Probabilistic graphical model-based security for SCADA critical infrastructures. (Doctoral Dissertation). Sorbonne université. Retrieved from http://www.theses.fr/2018SORUS502

Chicago Manual of Style (16^th Edition):

Cortijo Aragon, Santiago José. “Sécurité pour des infrastructures critiques SCADA fondée sur des modèles graphiques probabilistes : Probabilistic graphical model-based security for SCADA critical infrastructures.” 2018. Doctoral Dissertation, Sorbonne université. Accessed April 22, 2021. http://www.theses.fr/2018SORUS502.

MLA Handbook (7^th Edition):

Cortijo Aragon, Santiago José. “Sécurité pour des infrastructures critiques SCADA fondée sur des modèles graphiques probabilistes : Probabilistic graphical model-based security for SCADA critical infrastructures.” 2018. Web. 22 Apr 2021.

Vancouver:

Cortijo Aragon SJ. Sécurité pour des infrastructures critiques SCADA fondée sur des modèles graphiques probabilistes : Probabilistic graphical model-based security for SCADA critical infrastructures. [Internet] [Doctoral dissertation]. Sorbonne université; 2018. [cited 2021 Apr 22]. Available from: http://www.theses.fr/2018SORUS502.

Council of Science Editors:

Cortijo Aragon SJ. Sécurité pour des infrastructures critiques SCADA fondée sur des modèles graphiques probabilistes : Probabilistic graphical model-based security for SCADA critical infrastructures. [Doctoral Dissertation]. Sorbonne université; 2018. Available from: http://www.theses.fr/2018SORUS502

University of Southern California

7. Xia, Yinglong. Exploration of parallelism for probabilistic graphical models.

Degree: PhD, Computer Science, 2010, University of Southern California

URL: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/420474/rec/2647

► Probabilistic graphical models such as Bayesian networks and junction trees are widely used to compactly represent joint probability distributions. They have found applications in a… (more)

▼ Probabilistic graphical models such as Bayesian networks and junction trees are widely used to compactly represent joint probability distributions. They have found applications in a number of domains, including medical diagnosis, credit assessment, genetics, among others. The computational complexity of exact inference, a key problem in exploring probabilistic graphical models, increases dramatically with the density of the network, the clique width and the number of states of random variables. In many cases, exact inference must be performed in real time.; In this work, we explore parallelism for exact inference at various granularities on state-of-the-art high performance computing platforms. We first study parallel techniques for converting an arbitrary Bayesian network into a junction tree. Then, at a fine grained level, we explore data parallelism in node level primitives for exact inference in junction trees. Based on the node level primitives, we develop computation kernels for evidence collection and distribution on both clusters and multicore processors. In addition, we propose a junction tree decomposition approach for exact inference on a cluster of processors to explore structural parallelism at a coarse grained level. To utilize structural parallelism dynamically, we also develop various schedulers for exact inference. Specifically, we develop a centralized scheduler for heterogeneous processors, a lock-free collaborative scheduler for multicore processors, and a hierarchical scheduler with dynamic thread grouping for manycore processors. The schedulers balance the workload across the cores and partition large tasks at runtime to adapt to the processor architecture. Finally, for junction trees offering limited parallelism at both data and structural levels, we propose a pointer jumping based method for exact inference to accelerate evidence propagation.; We implemented our proposed methods using Pthreads and Message Passing Interface (MPI) on various platforms, including clusters, general-purpose multicore processors, heterogeneous multicore processors, and manycore processors. Compared with various baseline algorithms using a representative set of junction trees, our proposed methods exhibit superior performance. Advisors/Committee Members: Prasanna, Viktor K. (Committee Chair), Nakano, Aiichiro (Committee Member), Dubois, Michel (Committee Member).

Subjects/Keywords: parallel computing; parallel algorithm; probabilistic graphical model; exact inference; multicore processor; scheduler

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Xia, Y. (2010). Exploration of parallelism for probabilistic graphical models. (Doctoral Dissertation). University of Southern California. Retrieved from http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/420474/rec/2647

Chicago Manual of Style (16^th Edition):

Xia, Yinglong. “Exploration of parallelism for probabilistic graphical models.” 2010. Doctoral Dissertation, University of Southern California. Accessed April 22, 2021. http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/420474/rec/2647.

MLA Handbook (7^th Edition):

Xia, Yinglong. “Exploration of parallelism for probabilistic graphical models.” 2010. Web. 22 Apr 2021.

Vancouver:

Xia Y. Exploration of parallelism for probabilistic graphical models. [Internet] [Doctoral dissertation]. University of Southern California; 2010. [cited 2021 Apr 22]. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/420474/rec/2647.

Council of Science Editors:

Xia Y. Exploration of parallelism for probabilistic graphical models. [Doctoral Dissertation]. University of Southern California; 2010. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/420474/rec/2647

8. Felipe Iwao Takiyama. Algoritmos de inferência exata para modelos de primeira ordem.

Degree: 2014, University of São Paulo

URL: http://www.teses.usp.br/teses/disponiveis/3/3152/tde-23122014-145932/

►

Este trabalho descreve a implementação de algoritmos de inferência para modelos de primeira ordem. Três algoritmos foram implementados: ve, c-fove e ac-fove. Este último e… (more)

Subjects/Keywords: AC-FOVE; Algoritmo de eliminação de variáveis em primeira ordem; Algoritmo de inferência exata; AC-FOVE; Exact inference algorithm; First-order variable elimination algorithm

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APA (6^th Edition):

Takiyama, F. I. (2014). Algoritmos de inferência exata para modelos de primeira ordem. (Masters Thesis). University of São Paulo. Retrieved from http://www.teses.usp.br/teses/disponiveis/3/3152/tde-23122014-145932/

Chicago Manual of Style (16^th Edition):

Takiyama, Felipe Iwao. “Algoritmos de inferência exata para modelos de primeira ordem.” 2014. Masters Thesis, University of São Paulo. Accessed April 22, 2021. http://www.teses.usp.br/teses/disponiveis/3/3152/tde-23122014-145932/.

MLA Handbook (7^th Edition):

Takiyama, Felipe Iwao. “Algoritmos de inferência exata para modelos de primeira ordem.” 2014. Web. 22 Apr 2021.

Vancouver:

Takiyama FI. Algoritmos de inferência exata para modelos de primeira ordem. [Internet] [Masters thesis]. University of São Paulo; 2014. [cited 2021 Apr 22]. Available from: http://www.teses.usp.br/teses/disponiveis/3/3152/tde-23122014-145932/.

Council of Science Editors:

Takiyama FI. Algoritmos de inferência exata para modelos de primeira ordem. [Masters Thesis]. University of São Paulo; 2014. Available from: http://www.teses.usp.br/teses/disponiveis/3/3152/tde-23122014-145932/

9. Higgins, Michael. Applications of Integer Programming Methods to Solve Statistical Problems.

Degree: Statistics, 2013, University of California – Berkeley

URL: http://www.escholarship.org/uc/item/1g55c77q

► Many problems in statistics are inherently discrete. When one of these problems also contains an optimization component, integer programming may be used to facilitate a… (more)

▼ Many problems in statistics are inherently discrete. When one of these problems also contains an optimization component, integer programming may be used to facilitate a solution to the statistical problem. We use integer programming techniques to help solve problems in the following areas: optimal blocking of a randomized controlled experiment with several treatment categories and statistical auditing using stratified random samples.We develop a new method for blocking in randomized experiments that works for an arbitrary number of treatments. We analyze the following problem: given a threshold for the minimum number of units to be contained in a block, and given a distance measure between any two units in the finite population, block the units so that the maximum distance between any two units within a block is minimized. This blocking criterion can minimize covariate imbalance, which is a common goal in experimental design. Finding an optimal blocking is an NP-hard problem. However, using ideas from graph theory, we provide the first polynomial time approximately optimal blocking algorithm for when there are more than two treatment categories. In the case of just two such categories, our approach is more efficient than existing methods. We derive the variances of estimators for sample average treatment effects under the Neyman-Rubin potential outcomes model for arbitrary blocking assignments and an arbitrary number of treatments. In addition, statistical election audits can be used to collect evidence that the set of winners (the outcome) of an election according to the machine count is correct – that it agrees with the outcome that a full hand count of the audit trail would show. The strength of evidence is measured by the p<\italic>-value of the hypothesis that the machine outcome is wrong. Smaller p<\italic>-values are stronger evidence that the outcome is correct. Most states that have election audits of any kind require audit samples stratified by county for contests that cross county lines. Previous work on p<\italic>-values for stratified samples based on the largest weighted overstatement of the margin used upper bounds that can be quite weak. Sharper p-values than those found by previous work can be found by solving a 0-1 knapsack problem. We also give algorithms for choosing how many batches to draw from each stratum to reduce the counting burden.

Subjects/Keywords: Statistics; Blocking; Causal inference; Election auditing; Exact inference; Experimental design; Integer programming

…12 Exact and conservative p-values for hypothetical maximum observed overstatements (… …Senate race. The LKP conservative p-values (PLKP ) are nearly identical to the exact… …46 vi List of Tables 3.1 Conservative and exact p-values for the hypothesis that the… …x28;2008b). Column 3: LKP conservative p-value. Column 4: exact p-value obtained by solving… …exact p-values in Table 3.1 (0.0159 for maximum MRO and 0.0189 for maximum observed taint…

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Higgins, M. (2013). Applications of Integer Programming Methods to Solve Statistical Problems. (Thesis). University of California – Berkeley. Retrieved from http://www.escholarship.org/uc/item/1g55c77q

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Higgins, Michael. “Applications of Integer Programming Methods to Solve Statistical Problems.” 2013. Thesis, University of California – Berkeley. Accessed April 22, 2021. http://www.escholarship.org/uc/item/1g55c77q.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Higgins, Michael. “Applications of Integer Programming Methods to Solve Statistical Problems.” 2013. Web. 22 Apr 2021.

Vancouver:

Higgins M. Applications of Integer Programming Methods to Solve Statistical Problems. [Internet] [Thesis]. University of California – Berkeley; 2013. [cited 2021 Apr 22]. Available from: http://www.escholarship.org/uc/item/1g55c77q.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Higgins M. Applications of Integer Programming Methods to Solve Statistical Problems. [Thesis]. University of California – Berkeley; 2013. Available from: http://www.escholarship.org/uc/item/1g55c77q

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

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Open Access Theses and Dissertations