Jim Q. Smith

Bio: Jim Q. Smith is an academic researcher from University of Warwick. The author has contributed to research in topics: Bayesian network & Graphical model. The author has an hindex of 29, co-authored 183 publications receiving 4358 citations. Previous affiliations of Jim Q. Smith include Health Protection Agency & University College London.

FLOWERING LOCUS C Mediates Natural Variation in the High-Temperature Response of the Arabidopsis Circadian Clock

Abstract: Temperature compensation contributes to the accuracy of biological timing by preventing circadian rhythms from running more quickly at high than at low temperatures. We previously identified quantitative trait loci (QTL) with temperature-specific effects on the circadian rhythm of leaf movement, including a QTL linked to the transcription factor FLOWERING LOCUS C (FLC). We have now analyzed FLC alleles in near-isogenic lines and induced mutants to eliminate other candidate genes. We showed that FLC lengthened the circadian period specifically at 27 degrees C, contributing to temperature compensation of the circadian clock. Known upstream regulators of FLC expression in flowering time pathways similarly controlled its circadian effect. We sought to identify downstream targets of FLC regulation in the molecular mechanism of the circadian clock using genome-wide analysis to identify FLC-responsive genes and 3503 transcripts controlled by the circadian clock. A Bayesian clustering method based on Fourier coefficients allowed us to discriminate putative regulatory genes. Among rhythmic FLC-responsive genes, transcripts of the transcription factor LUX ARRHYTHMO (LUX) correlated in peak abundance with the circadian period in flc mutants. Mathematical modeling indicated that the modest change in peak LUX RNA abundance was sufficient to cause the period change due to FLC, providing a molecular target for the crosstalk between flowering time pathways and circadian regulation.

Decision Analysis: A Bayesian Approach

Abstract: The rudiments of decision analysis. Decision trees. Utilities and rewards. Subjective probabilities and their measurement. Influence diagrams, group decisions and some practical problems in decision analysis. Bayesian statistics for decision analysis. Bayes estimation.

Diagnostic checks of non‐standard time series models

Abstract: Diagnostic checks have become a standard tool for helping to assess the adequacy of a forecasting system since Box and Jenkins' (1970) ARIMA modelling technique became popular. However, most of the research has developed checks for normal or second-order stationary models. This paper gives various diagnostic checks that can be performed simply on nonnormal, non-standard models such as the class of multiprocess models (Harrison and Stevens, 1976), where residuals are definitely not normal. The performance to date of these models can then be objectively scrutinized on-line. Examples, including a generalized cusum technique, are given to illustrate the effectiveness of the techniques on specific series.

Inference from Iterative Simulation Using Multiple Sequences

Abstract: The Gibbs sampler, the algorithm of Metropolis and similar iterative simulation methods are potentially very helpful for summarizing multivariate distributions. Used naively, however, iterative simulation can give misleading answers. Our methods are simple and generally applicable to the output of any iterative simulation; they are designed for researchers primarily interested in the science underlying the data and models they are analyzing, rather than for researchers interested in the probability theory underlying the iterative simulations themselves. Our recommended strategy is to use several independent sequences, with starting points sampled from an overdispersed distribution. At each step of the iterative simulation, we obtain, for each univariate estimand of interest, a distributional estimate and an estimate of how much sharper the distributional estimate might become if the simulations were continued indefinitely. Because our focus is on applied inference for Bayesian posterior distributions in real problems, which often tend toward normality after transformations and marginalization, we derive our results as normal-theory approximations to exact Bayesian inference, conditional on the observed simulations. The methods are illustrated on a random-effects mixture model applied to experimental measurements of reaction times of normal and schizophrenic patients.

emcee: The MCMC Hammer

Abstract: We introduce a stable, well tested Python implementation of the affine-invariant ensemble sampler for Markov chain Monte Carlo (MCMC) proposed by Goodman & Weare (2010). The code is open source and has already been used in several published projects in the astrophysics literature. The algorithm behind emcee has several advantages over traditional MCMC sampling methods and it has excellent performance as measured by the autocorrelation time (or function calls per independent sample). One major advantage of the algorithm is that it requires hand-tuning of only 1 or 2 parameters compared to ~N2 for a traditional algorithm in an N-dimensional parameter space. In this document, we describe the algorithm and the details of our implementation. Exploiting the parallelism of the ensemble method, emcee permits any user to take advantage of multiple CPU cores without extra effort. The code is available online at http://dan.iel.fm/emcee under the GNU General Public License v2.

Novel approach to nonlinear/non-Gaussian Bayesian state estimation

Abstract: An algorithm, the bootstrap filter, is proposed for implementing recursive Bayesian filters. The required density of the state vector is represented as a set of random samples, which are updated and propagated by the algorithm. The method is not restricted by assumptions of linear- ity or Gaussian noise: it may be applied to any state transition or measurement model. A simula- tion example of the bearings only tracking problem is presented. This simulation includes schemes for improving the efficiency of the basic algorithm. For this example, the performance of the bootstrap filter is greatly superior to the standard extended Kalman filter.

General methods for monitoring convergence of iterative simulations

Abstract: We generalize the method proposed by Gelman and Rubin (1992a) for monitoring the convergence of iterative simulations by comparing between and within variances of multiple chains, in order to obtain a family of tests for convergence. We review methods of inference from simulations in order to develop convergence-monitoring summaries that are relevant for the purposes for which the simulations are used. We recommend applying a battery of tests for mixing based on the comparison of inferences from individual sequences and from the mixture of sequences. Finally, we discuss multivariate analogues, for assessing convergence of several parameters simultaneously.

Thinking fast and slow.

Abstract: In 1974 an article appeared in Science magazine with the dry-sounding title “Judgment Under Uncertainty: Heuristics and Biases” by a pair of psychologists who were not well known outside their discipline of decision theory. In it Amos Tversky and Daniel Kahneman introduced the world to Prospect Theory, which mapped out how humans actually behave when faced with decisions about gains and losses, in contrast to how economists assumed that people behave. Prospect Theory turned Economics on its head by demonstrating through a series of ingenious experiments that people are much more concerned with losses than they are with gains, and that framing a choice from one perspective or the other will result in decisions that are exactly the opposite of each other, even if the outcomes are monetarily the same. Prospect Theory led cognitive psychology in a new direction that began to uncover other human biases in thinking that are probably not learned but are part of our brain’s wiring.