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Devices for Removal of VOCs from Ground Water (Spring 2002) IPRO 304B
Water in Wausau, Wisconsin is contaminated with VOCs posing hazardous health risks. As part of the IPRO project, we need to determine a unit operation that can remove VOCs effectively and cost efficiently., Sponsorship: IIT Interprofessional Collaboratory, Project Plan for IPRO 304B: Devices for Removal of VOCs from Ground Water for the the Spring 2002 semester
Design and Evaluation of New Flue Gas Cleanup Processes to Meet New EPA Regulations (Spring 2003) IPRO 304
Students who join this IPRO team will gain experience in working with chemical and environmental engineering students on important issues that relate to designing chemical and environmental engineering processes. Studens on this project will be split into three different subteams: (a) Design and Evaluation of New Flue Gas Cleanup processes to meet new Environmental Regulations: The objective of this project is to design and provide an economic assessment of new flue gas cleanup processes to meet new and future environmental emissions standards. The issues to be considered in this project include technical viability, process integration, economic feasibility, and environmental disposal of waste streams. An economic assessment of the market-based prices of emissions allowances will be used to determine the best long-run strategy. An assessment will be made on the effects of implementation of new technologies on the cost of electricity for both low sulfur western coal, and high sulfur Illinois coal to determine if governmental incentives are needed to promote the use of Illinois coal. (b) Design and evaluation of engineering systems to control VOCs from groundwater: The objective of this project is the design and cost estimation for various pollution control devices that can remove volatile organic chemicals (VOCs) from ground water. Effective and efficient treatment methods are needed to meet this clean up challenge. (c) Design of a Modern Hydrogen Production and Recovery Facility: The object of this project is to apply engineering principles of separation processes to recove pure hydrogen from mixed gases. This will include the availability and selection of a feed stream and an analysis of the feasibility and economics of commercial and innovative processed for the recovery operation., Sponsorship: IIT Collaboratory for Interprofessional Studies, Project Plan for IPRO 304: Design and Evaluation of New Flue Gas Cleanup Processes to Meet New EPA Regulations for the Spring 2003 semester
Design and Evaluation of Engineering Systems to Remove VOCs from Groundwater (Spring 2003) IPRO 304B
The objective of this project is the design and cost estimation for various pollution control devices that can remove volatile organic chemicals (VOCs) from ground water. Effective and efficient treatment methods are needed to meet this clean up challenge. Full-scale performance and cost data that minimizes energy requirements and documents the costs associated with design, construction, and operation will be required. Integration of various technologies and the application of unit operations involving adsorption, absorption, and biological reactor systems will be addressed. The need to address present and future environmental emission standards will be integrated into the design procedures. The design process will be part of a general evaluation of emission standards required for the control and removal of hazardous waste., Sponsorship: IIT Collaboratory for Interprofessional Studies, Project Plan for IPRO 304B: Design and Evaluation of Engineering Systems to Remove VOCs from Groundwater for the Spring 2003 semester
Binary hidden Markov models and varieties
This paper closely examines HMMs in which all the hidden random variables are binary. Its main contributions are (1) a birational parametrization for every such HMM, with an explicit inverse for recovering the hidden parameters in terms of observables, (2) a semialgebraic model membership test for every such HMM, and (3) minimal dening equations for the 4-node fully binary model, comprising 21 quadrics and 29 cubics, which were computed using Grobner bases in the cumulant coordinates of Sturmfels and Zwiernik. The new model parameters in (1) are rationally identiable in the sense of Sullivant, Garcia-Puente, and Spielvogel, and each model's Zariski closure is therefore a rational projective variety of dimension 5. Grobner basis computations for the model and its graph are found to be considerably faster using these parameters. In the case of two hidden states, item (2) supersedes a previous algorithm of Schonhuth which is only generically dened, and the dening equations (3) yield new invariants for HMMs of all lengths 4. Such invariants have been used successfully in model selection problems in phylogenetics, and one can hope for similar applications in the case of HMMs.
On Polyhedral Approximations of Polytopes for Learning Bayesian Networks
The motivation for this paper is the geometric approach to statistical learning Bayesiannetwork (BN) structures. We review three vector encodings of BN structures. The first one has been used by Jaakkola et al. [9] and also by Cussens [4], the other two use special integral vectors formerly introduced, called imsets [18, 20]. The topic is the comparison of outer polyhedral approximations of the corresponding polytopes. We show how to transform the inequalities suggested by Jaakkola et al. [9] into the framework of imsets. The result of our comparison is the observation that the implicit polyhedral approximation of the standard imset polytope suggested in [21] gives a tighter approximation than the (transformed) explicit polyhedral approximation from [9]. As a consequence, we confirm a conjecture from [21] that the above-mentioned implicit polyhedral approximation of the standard imset polytope is an LP relaxation of that polytope. In the end, we review recent attempts to apply the methods of integer programming to learning BN structures and discuss the task of finding suitable explicit LP relaxation in the imset-based approach.
Learning Coefficient in Bayesian Estimation of Restricted Boltzmann Machine
We consider the real log canonical threshold for the learning model in Bayesian estimation. This threshold corresponds to a learning coefficient of generalization error in Bayesian estimation, which serves to measure learning efficiency in hierarchical learning models [30, 31, 33]. In this paper, we clarify the ideal which gives the log canonical threshold of the restricted Boltzmann machine and consider the learning coefficients of this model.
Phylogenetic invariants for group-based models
In this paper we investigate properties of algebraic varieties representing group-based phylogenetic models. We propose a method of generating many phylogenetic invariants. We prove that we obtain all invariants for any tree for the two-state Jukes-Cantor model. We conjecture that for a large class of models our method can give all phylogenetic invariants for any tree. We show that for 3-Kimura our conjecture is equivalent to the conjecture of Sturmfels and Sullivant [22, Conjecture 2]. This, combined with the results in [22], would make it possible to determine all phylogenetic invariants for any tree for 3-Kimura model, and also other phylogenetic models. Next we give the (first) examples of non-normal varieties associated to general group-based model for an abelian group. Following Kubjas [17] we prove that for many group-based models varieties associated to trees with the same number of leaves do not have to be deformation equivalent.
Properties of semi-elementary imsets as sums of elementary imsets
We study properties of semi-elementary imsets and elementary imsets introduced by Studeny [10]. The rules of the semi-graphoid axiom (decomposition, weak union and contraction) for conditional independence statements can be translated into a simple identity among three semi-elementary imsets. By recursively applying the identity, any semi-elementary imset can be written as a sum of elementary imsets, which we call a representation of the semi-elementary imset. A semi-elementary imset has many representations. We study properties of the set of possible representations of a semi-elementary imset and prove that all representations are connected by relations among four elementary imsets.
Higher Connectivity of Fiber Graphs of Gröbner Bases
Fiber graphs of Gröbner bases from contingency tables are important in statistical hypothesis testing, where one studies random walks on these graphs using the Metropolis-Hastings algorithm. The connectivity of the graphs has implications on how fast the algorithm converges. In this paper, we study a class of ber graphs with elementary combinatorial techniques and provide results that support a recent conjecture of Engström: the connectivity is given by the minimum vertex degree.
An Iterative Method Converging to a Positive Solution of Certain Systems of Polynomial Equations
We present a numerical algorithm for finding real non-negative solutions to a certain class of polynomial equations. Our methods are based on the expectation maximization and iterative proportional fitting algorithms, which are used in statistics to find maximum likelihood parameters for certain classes of statistical models. Since our algorithm works by iteratively improving an approximate solution, we find approximate solutions in the cases when there are no exact solutions, such as overconstrained systems.
Geometry of Higher-Order Markov Chains
We determine an explicit Gr ?obner basis, consisting of linear forms and determinantal quadrics, for the prime ideal of Raftery’s mixture transition distribution model for Markov chains. When the states are binary, the corresponding projective variety is a linear space, the model itself consists of two simplices in a cross-polytope, and the likelihood function typically has two local maxima. In the general non-binary case, the model corresponds to a cone over a Segre variety.
Betti Numbers of Cut Ideals of Trees
Cut ideals, introduced by Sturmfels and Sullivant, are used in phylogenetics and algebraic statistics. We study the minimal free resolutions of cut ideals of tree graphs. By employing basic methods from topological combinatorics, we obtain upper bounds for the Betti numbers of this type of ideals. These take the form of simple formulas on the number of vertices, which arise from the enumeration of induced subgraphs of certain incomparability graphs associated to the edge sets of trees.
The geometry of Sloppiness
The use of mathematical models in the sciences often requires the estimation of unknown parameter values from data. Sloppiness provides information about the uncertainty of this task. In this paper, we develop a precise mathematical foundation for sloppiness and define rigorously its key concepts, such as `model manifold', in relation to concepts of structural identifiability. We redefine sloppiness conceptually as a comparison between the premetric on parameter space induced by measurement noise and a reference metric. This opens up the possibility of alternative quantification of sloppiness, beyond the standard use of the Fisher Information Matrix, which assumes that parameter space is equipped with the usual Euclidean and the measurement error is infinitesimal. Applications include parametric statistical models, explicit time dependent models, and ordinary differential equation models.
Mixture models for rating data: the method of moments via Groebner bases
A recent thread of research in ordinal data analysis involves a class of mixture models that designs the responses as the combination of the two main aspects driving the decision pro- cess: a feeling and an uncertainty components. This novel paradigm has been proven flexible to account also for overdispersion. In this context, Groebner bases are exploited to estimate model parameters by implementing the method of moments. In order to strengthen the validity of the moment procedure so derived, alternatives parameter estimates are tested by means of a simulation experiment. Results show that the moment estimators are satisfactory per se, and that they significantly reduce the bias and perform more efficiently than others when they are set as starting values for the Expectation-Maximization algorithm.
Ideal-Theoretic Strategies for Asymptotic Approximation of Marginal Likelihood Integrals
The accurate asymptotic evaluation of marginal likelihood integrals is a fundamental problem in Bayesian statistics. Following the approach introduced by Watanabe, we translate this into a problem of computational algebraic geometry, namely, to determine the real log canonical threshold of a polynomial ideal, and we present effective methods for solving this problem. Our results are based on resolution of singularities. They apply to parametric models where the Kullback-Leibler distance is upper and lower bounded by scalar multiples of some sum of squared real analytic functions. Such models include finite state discrete models.
Unimodular hierarchical models and their Graver bases
Given a simplicial complex whose vertices are labeled with positive integers, one can associate a vector configuration whose corresponding toric variety is the Zariski closure of a hierarchical model. We classify all the vertex-weighted simplicial complexes that give rise to unimodular vector configurations. We also provide a combinatorial characterization of their Graver bases.
Mixtures and products in two graphical models
We compare two statistical models of three binary random variables. One is a mixture model and the other is a product of mixtures model called a restricted Boltzmann machine. Although the two models we study look different from their parametrizations, we show that they represent the same set of distributions on the interior of the probability simplex, and are equal up to closure. We give a semi-algebraic description of the model in terms of six binomial inequalities and obtain closed form expressions for the maximum likelihood estimates. We briefly discuss extensions to larger models.
Markov bases for two-way change-point models of ladder determinantal tables
To evaluate the goodness-of-fit of a statistical model to given data, calculating a conditional p value by a Markov chain Monte Carlo method is one of the effective approaches. For this purpose, a Markov basis plays an important role because it guarantees the connectivity of the chain, which is needed for unbiasedness of the estimation, and therefore is investigated in various settings such as incomplete tables or subtable sum constraints. In this paper, we consider the two-way change-point model for the ladder determinantal table, which is an extension of these two previous works, i.e., works on incomplete tables by Aoki and Takemura (2005, J. Stat. Comput. Simulat.) and subtable some constraints by Hara, Takemura and Yoshida (2010, J. Pure Appl. Algebra). Our main result is based on the theory of Gr ?obner basis for the distributive lattice. We give a numerical example for actual data.
Cubature Rules and Expected Value of Some Complex Functions
The expected value of some complex valued random vectors is computed by means of the indicator function of a designed experiment as known in algebraic statistics. The general theory is set-up and results are obtained for finite discrete random vectors and the Gaussian random vector. The precision space of some cubature rules/designed experiments is determined.
Strongly Robust Toric Ideals in Codimension 2
A homogeneous ideal is robust if its universal Gr ?obner basis is also a minimal generating set. For toric ideals, one has the stronger definition: A toric ideal is strongly robust if its Graver basis equals the set of indispensable binomials. We characterize the codimension 2 strongly robust toric ideals by their Gale diagrams. This gives a positive answer to a question of Petrovi?, Thoma, and Vladoiu in the case of codimension 2 toric ideals.

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