31,670 research outputs found
Michael Rodriguez interviews fiction writer Michael Kimball
No full textAuthor Michael Kimball talks about moving away from Michigan to become a successful writer, his education, the fiction reading series he has started in Baltimore, the life-story-on-postcard project, and his book "Dear everybody." Kimball is interviewed by Michigan State University Librarian Michael Rodriguez for the Michigan State University Libraries' Michigan Writers Series
Control Variates for Stochastic Simulation of Chemical Reaction Networks
No full textStochastic simulation is a widely used method for estimating quantities in models of chemical reaction networks where uncertainty plays a crucial role. However, reducing the statistical uncertainty of the corresponding estimators requires the generation of a large number of simulation runs, which is computationally expensive. To reduce the number of necessary runs, we propose a variance reduction technique based on control variates. We exploit constraints on the statistical moments of the stochastic process to reduce the estimators’ variances. We develop an algorithm that selects appropriate control variates in an on-line fashion and demonstrate the efficiency of our approach on several case studies
Michael Rodriguez interviews author Paul Clemens
No full textAuthor Paul Clemens talks about his book "Made in Detroit," the genre of memoir, and writing about race. Clemens is interviewed by Michigan State University Librarian Michael Rodriguez for the MSU Libraries' Michigan Writers Series. Held in the MSU Main Library
Michael Rodriguez interviews author Tom Springer
No full textAuthor Tom Springer is interviewed about his writing career and his newest book "Looking for hickories". Springer talks about his career following after earning an Environmental Journalism degree from Michigan State University. He calls his genre "creative non-fiction" and explains how he weaves his memories into his books about life in rural and wild Michigan. Part of the Michigan State University Libraries' Michigan Writers Series. Springer is interviewed by Librarian Michael Rodriguez
Michael Rodriguez interviews author Gary Gildner
No full textAuthor Gary Gildner explains why he left his tenured teaching position to move to Idaho to became a full-time writer of poetry. Gildner talks about donating his personal papers to Michigan State University Libraries' Special Collections, his writing style and how he approaches writing. Gildner is interviewed by MSU Librarian Michael Rodriguez for the MSU Libraries' Michigan Writer Series. Held at the MSU Main Library
Gold standard of UK degrees is lost in translation
No full textInflated marks, overworked staff and politically compromised courses are the price of exploiting offshore UK registered students, says Michael Day
Michael Rodriguez interviews historian and author Keith Widder
No full textHistorian and author Keith Widder talks about his move to Michigan from Wisconsin, his career as Curator of History for the Mackinac Island State Park Commission, his research interests, his book "Michigan Agricultural College", and his current projects. Widder is interviewed by Michigan State University Librarian Michael Rodriguez for the MSU Libraries' Michigan Writers Series. Held in the MSU Main Library
Analysis of Markov Jump Processes under Terminal Constraints
No full textMany probabilistic inference problems such as stochastic filtering or the computation of rare event probabilities require model analysis under initial and terminal constraints. We propose a solution to this bridging problem for the widely used class of population-structured Markov jump processes. The method is based on a state-space lumping scheme that aggregates states in a grid structure. The resulting approximate bridging distribution is used to iteratively refine relevant and truncate irrelevant parts of the state-space. This way, the algorithm learns a well-justified finite-state projection yielding guaranteed lower bounds for the system behavior under endpoint constraints. We demonstrate the method’s applicability to a wide range of problems such as Bayesian inference and the analysis of rare events
Generalized method of moments for stochastic reaction networks in equilibrium
No full textCalibrating parameters is a crucial problem within quantitative modeling approaches to reaction networks. Existing methods for stochastic models rely either on statistical sampling or can only be applied to small systems. Here we present an inference procedure for stochastic models in equilibrium that is based on a moment matching scheme with optimal weighting and that can be used with high-throughput data like the one collected by flow cytometry. Our method does not require an approximation of the underlying equilibrium probability distribution and, if reaction rate constants have to be learned, the optimal values can be computed by solving a linear system of equations. We evaluate the effectiveness of the proposed approach on three case studies
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