Brad Bell

Bio: Brad Bell is an academic researcher from University of Washington. The author has contributed to research in topics: Automatic differentiation & Random effects model. The author has an hindex of 7, co-authored 7 publications receiving 4828 citations.

TMB: Automatic Differentiation and Laplace Approximation

Abstract: TMB is an open source R package that enables quick implementation of complex nonlinear random effects (latent variable) models in a manner similar to the established AD Model Builder package (ADMB, http://admb-project.org/; Fournier et al. 2011). In addition, it offers easy access to parallel computations. The user defines the joint likelihood for the data and the random effects as a C++ template function, while all the other operations are done in R; e.g., reading in the data. The package evaluates and maximizes the Laplace approximation of the marginal likelihood where the random effects are automatically integrated out. This approximation, and its derivatives, are obtained using automatic differentiation (up to order three) of the joint likelihood. The computations are designed to be fast for problems with many random effects (≈ 106 ) and parameters (≈ 103 ). Computation times using ADMB and TMB are compared on a suite of examples ranging from simple models to large spatial models where the random effects are a Gaussian random field. Speedups ranging from 1.5 to about 100 are obtained with increasing gains for large problems. The package and examples are available at http://tmb-project.org/.

TMB: Automatic Differentiation and Laplace Approximation

Abstract: TMB is an open source R package that enables quick implementation of complex nonlinear random effect (latent variable) models in a manner similar to the established AD Model Builder package (ADMB, this http URL). In addition, it offers easy access to parallel computations. The user defines the joint likelihood for the data and the random effects as a C++ template function, while all the other operations are done in R; e.g., reading in the data. The package evaluates and maximizes the Laplace approximation of the marginal likelihood where the random effects are automatically integrated out. This approximation, and its derivatives, are obtained using automatic differentiation (up to order three) of the joint likelihood. The computations are designed to be fast for problems with many random effects (~10^6) and parameters (~10^3). Computation times using ADMB and TMB are compared on a suite of examples ranging from simple models to large spatial models where the random effects are a Gaussian random field. Speedups ranging from 1.5 to about 100 are obtained with increasing gains for large problems. The package and examples are available at this http URL.

Trivial persuasion in the courtroom: the power of (a few) minor details.

Abstract: Investigated the influence of trivial testimonial detail on judgments of 424 undergraduates who served as mock jurors. Ss read a summary of a court case involving robbery and murder. In Experiment 1, detailed testimony influenced judgments of guilt, even when the detail was unrelated to the culprit. In Experiment 2, detailed testimony was especially powerful when an opposing witness testified that she could not remember the trivial details. Subsequent analyses suggest that the impact of detailed testimony on guilt judgments is mediated by inferences about the eyewitnesses. When eyewitnesses provided more detail, they were generally judged to be more credible, to have a better memory for the culprit's face and for details, and to have paid more attention to the culprit. Language: en

Degree of Detail of Eyewitness Testimony and Mock Juror Judgments1

Abstract: The influence of the degree of detail of eyewitness testimony on two sides of a court case was investigated in two experiments. In the first experiment subject-jurors read a civil court case involving an automobile-pedestrian accident. The plaintiff and the defendant presented conflicting eyewitness accounts. Judgments of the relative credibility of the eyewitnesses on each side and the percentage of negligence of the parties were influenced by the relative degree of detail of the eyewitness testimony on each side. In the second experiment subject-jurors read a criminal court case involving robbery and murder. The prosecution and defense presented conflicting eyewitness accounts. The degree of detail of the prosecution eyewitness testimony influenced judgments of guilt and judgments of the credibility of the eyewitnesses. An examination of the reasons for verdicts and credibility judgments revealed that some subjects inferred that an eyewitness who gave testimony with a greater degree of detail had a better memory for the trivial details and the culprit than an eyewitness who gave testimony with a lesser degree of detail. Implications of these results for the legal system are discussed.

Heart Disease and Stroke Statistics—2017 Update: A Report From the American Heart Association

Abstract: WRITING GROUP MEMBERS Emelia J. Benjamin, MD, SCM, FAHA Michael J. Blaha, MD, MPH Stephanie E. Chiuve, ScD Mary Cushman, MD, MSc, FAHA Sandeep R. Das, MD, MPH, FAHA Rajat Deo, MD, MTR Sarah D. de Ferranti, MD, MPH James Floyd, MD, MS Myriam Fornage, PhD, FAHA Cathleen Gillespie, MS Carmen R. Isasi, MD, PhD, FAHA Monik C. Jiménez, ScD, SM Lori Chaffin Jordan, MD, PhD Suzanne E. Judd, PhD Daniel Lackland, DrPH, FAHA Judith H. Lichtman, PhD, MPH, FAHA Lynda Lisabeth, PhD, MPH, FAHA Simin Liu, MD, ScD, FAHA Chris T. Longenecker, MD Rachel H. Mackey, PhD, MPH, FAHA Kunihiro Matsushita, MD, PhD, FAHA Dariush Mozaffarian, MD, DrPH, FAHA Michael E. Mussolino, PhD, FAHA Khurram Nasir, MD, MPH, FAHA Robert W. Neumar, MD, PhD, FAHA Latha Palaniappan, MD, MS, FAHA Dilip K. Pandey, MBBS, MS, PhD, FAHA Ravi R. Thiagarajan, MD, MPH Mathew J. Reeves, PhD Matthew Ritchey, PT, DPT, OCS, MPH Carlos J. Rodriguez, MD, MPH, FAHA Gregory A. Roth, MD, MPH Wayne D. Rosamond, PhD, FAHA Comilla Sasson, MD, PhD, FAHA Amytis Towfighi, MD Connie W. Tsao, MD, MPH Melanie B. Turner, MPH Salim S. Virani, MD, PhD, FAHA Jenifer H. Voeks, PhD Joshua Z. Willey, MD, MS John T. Wilkins, MD Jason HY. Wu, MSc, PhD, FAHA Heather M. Alger, PhD Sally S. Wong, PhD, RD, CDN, FAHA Paul Muntner, PhD, MHSc On behalf of the American Heart Association Statistics Committee and Stroke Statistics Subcommittee Heart Disease and Stroke Statistics—2017 Update