Seminars

2018-02-22 - Robert Richardson - Non-Gaussian Translation Processes

Presenter:

Robert Richardson

Title:

Non-Gaussian Translation Processes

Affiliation:

BYU

Date:

Feb 22, 2018

Abstract:

A non-Gaussian translation process is a method used in some engineering applications where a stochastic process is used with non-Gaussian marginal distributions. It could be considered a hierarchical copula model where the correlation structure of the process is defined separately from the marginal distributional characteristics. This approach also yields a simple likelihood function for the finite dimensional distributions of the stochastic process. These processes will be shown in a few applications to either perform tasks that could not be done previously or to do it much more efficiently such as non-Gaussian option pricing, general multivariate stable spatial processes, and non-Gaussian spatio-temporal dynamic modeling.

Website:

Dr. Richardson's Website

2018-02-15 - Jeffery Tessem - How to make more beta cells: exploring molecular pathways that increase functional beta cell mass as a cure for Type 1 and Type 2 diabetes

Presenter:

Dr. Jeffery S Tessem

Title:

How to make more beta cells: exploring molecular pathways that increase functional beta cell mass as a cure for Type 1 and Type 2 diabetes

Affiliation:

Department of Nutrition, Dietetics and Food Science at BYU

Date:

Feb 15, 2018

Abstract:

Both Type 1 (T1D) and Type 2 diabetes (T2D) are caused by a relative insufficiency in functional β-cell mass. Current therapeutic options for diabetes include daily insulin injections to maintain normoglycemia, pharmacological agents to stimulate β-cell function and enhance insulin sensitivity, or islet transplantation. A major obstacle to greater application of islet transplantation therapy is the scarcity of human islets. Thus, new methods for expansion of β-cell mass, applied in vitro to generate the large numbers of human islet cells needed for transplantation, or in situ to induce expansion of the patients remaining β-cells, could have broad therapeutic implications for this disease. To this end, our lab is interested in delineating the molecular pathways that increase β-cell proliferation, enhance glucose stimulated insulin secretion, and protect against β-cell death.

Website:

Dr. Tessem's Website

2018-02-08 - Chris Groendyke - Bayesian Inference for Contact Network Models using Epidemic Data

Presenter:

Chris Groendyke

Title:

Bayesian Inference for Contact Network Models using Epidemic Data

Affiliation:

Robert Morris University

Date:

Feb 8, 2018

Abstract:

I will discuss how network models can be used to study the spread of epidemics through a population, and in turn what epidemics can tell us about the structure of this population. I apply a Bayesian methodology to data from a disease presumed to have spread across a contact network in a population in order to perform inference on the parameters of the underlying network and disease models. Using a simulation study, I will discuss the strengths, weaknesses, and limitations of this type of these models, and the data required for this type of inference. Finally, I will describe an analysis of an actual measles epidemic that spread through the town of Hagelloch, Germany, in 1861 and share the conclusions it allows us to make regarding the population structure.

Website:

Chris's Website

2018-02-01 - Larry Baxter - Structure in Prior PDFs and Its Effect on Bayesian Analysis

Presenter:

Larry Baxter

Title:

Structure in Prior PDFs and Its Effect on Bayesian Analysis

Affiliation:

BYU

Date:

Feb 1, 2018

Abstract:

Bayesian statistics formalizes a procedure for combining established (prior) statistical knowledge with current knowledge to produce a posterior statistical description that presumably is better than either the prior or new knowledge by itself. Two common applications of this theory involve (a) combining established (literature) estimates of model parameter with new data to produce better parameter estimates, and (b) estimating model prediction confidence bands. Frequently, the prior information includes reasonable parameter estimates, poorly quantified and often subjective parameter uncertainty estimates, and no information regarding how the values of one parameter affect the confidence intervals of other parameters. All three of these parameter characteristics affect Bayesian analysis. The first two receive a great deal of attention. The third characteristic, the dependence of model parameters on one another, creates structure in the prior pdfs. This structure strongly influences Bayesian results, often to an extent that rivals or surpasses the parameter uncertainty best estimates. Nevertheless, Bayesian analyses commonly ignore this structure.

All structure stems primarily from the form of the model and, in linear models, does not depend on the observations themselves. Most models produce correlated parameters when applied to real-world engineering and science data. The most common example of structure is parameter correlation coefficients. Linear models produce linear parameter correlations that depend on the magnitude of the independent variable under analysis but that in most practical applications produce large, often close to unity, correlation coefficients. Nonlinear models also generally have correlated parameters. However the correlations can be nonlinear, even discontinuous, and generally involve more complexity than linear model parameter correlations. Parameter correlations profoundly affect the results of Bayesian parameter estimation and prediction uncertainty. Properly incorporated structure produces Bayesian results that powerfully illustrate the strength and potential contribution of the theory. Bayesian analyses that ignore such structure produce poor or even nonsensical results, often significantly worse than a superficial guess.

This seminar demonstrates the importance of prior structure in both parameter estimation and uncertainty quantification using real data from typical engineering systems. Perhaps most importantly, the discussion illustrates methods of incorporating parameter structure for any given model that does not rely on observations. These methods quantify parameter structure, including the lack of structure, for linear and nonlinear models.

Website:

Larry's Website

2018-01-18 - Brad Barney - Growing Curve Methodology with Application to Neonatal Growth Curves

Presenter:

Brad Barney

Title:

Growing Curve Methodology with Application to Neonatal Growth Curves

Affiliation:

BYU

Date:

Jan 18, 2018

Abstract:

As part of postnatal care, newborns are routinely monitored to assess the stability and adequacy of their growth. Interest lies in learning about the typical postnatal growth of especially preterm infants. We briefly consider some general methodological strategies currently employed to parsimoniously construct growth curves for use in medical practice. We present original results using existing methodology known as generalized additive models for location, scale and shape (GAMLSS). We also expand existing methodology on the Bayesian analogue of GAMLSS, known as structured additive distributional regression. In particular, we hierarchically model weight and length jointly, from which we are able to induce a time-varying distribution for Body Mass Index.

Co-Authors:

Adrienne Williamson, Josip Derado, Gregory Saunders, Irene Olsen, Reese Clark, Louise Lawson, Garritt Page, and Miguel de Carvalho

Website:

Brad's page