Bayes Factors for Forensic Decision Analyses with R

Bayes Factors for Forensic Decision Analyses with R provides a self-contained introduction to computational Bayesian statistics using R. With its primary focus on Bayes factors supported by data sets, this book features an operational perspective, practical relevance, and applicability—keeping theoretical and philosophical justifications limited. It offers a balanced approach to three naturally interrelated topics: – Probabilistic Inference: Relies on the core concept of Bayesian inferential statistics, to help practicing forensic scientists in the logical and balanced evaluation of the weight of evidence. – Decision Making: Features how Bayes factors are interpreted in practical applications to help address questions of decision analysis involving the use of forensic science in the law. – Operational Relevance: Combines inference and decision, backed up with practical examples and complete sample code in R, including sensitivity analyses and discussion on how to interpret results in context. Over the past decades, probabilistic methods have established a firm position as a reference approach for the management of uncertainty in virtually all areas of science, including forensic science, with Bayes' theorem providing the fundamental logical tenet for assessing how new information—scientific evidence—ought to be weighed. Central to this approach is the Bayes factor, which clarifies the evidential meaning of new information, by providing a measure of the change in the odds in favor of a proposition of interest, when going from the prior to the posterior distribution. Bayes factors should guide the scientist's thinking about the value of scientific evidence and form the basis of logical and balanced reporting practices, thus representing essential foundations for rational decision making under uncertainty. This book would be relevant to students, practitioners, and applied statisticians interested in inference and decision analyses in the critical field of forensic science. It could be used to support practical courses on Bayesian statistics and decision theory at both undergraduate and graduate levels, and will be of equal interest to forensic scientists and practitioners of Bayesian statistics for driving their evaluations and the use of R for their purposes

Bayes Factors for Forensic Decision Analyses with R

Bayes Factors for Forensic Decision Analyses with R provides a self-contained introduction to computational Bayesian statistics using R. With its primary focus on Bayes factors supported by data sets, this book features an operational perspective, practical relevance, and applicability—keeping theoretical and philosophical justifications limited. It offers a balanced approach to three naturally interrelated topics: – Probabilistic Inference: Relies on the core concept of Bayesian inferential statistics, to help practicing forensic scientists in the logical and balanced evaluation of the weight of evidence. – Decision Making: Features how Bayes factors are interpreted in practical applications to help address questions of decision analysis involving the use of forensic science in the law. – Operational Relevance: Combines inference and decision, backed up with practical examples and complete sample code in R, including sensitivity analyses and discussion on how to interpret results in context. Over the past decades, probabilistic methods have established a firm position as a reference approach for the management of uncertainty in virtually all areas of science, including forensic science, with Bayes' theorem providing the fundamental logical tenet for assessing how new information—scientific evidence—ought to be weighed. Central to this approach is the Bayes factor, which clarifies the evidential meaning of new information, by providing a measure of the change in the odds in favor of a proposition of interest, when going from the prior to the posterior distribution. Bayes factors should guide the scientist's thinking about the value of scientific evidence and form the basis of logical and balanced reporting practices, thus representing essential foundations for rational decision making under uncertainty. This book would be relevant to students, practitioners, and applied statisticians interested in inference and decision analyses in the critical field of forensic science. It could be used to support practical courses on Bayesian statistics and decision theory at both undergraduate and graduate levels, and will be of equal interest to forensic scientists and practitioners of Bayesian statistics for driving their evaluations and the use of R for their purposes

Bayes Factors for Forensic Decision Analyses with R

Bayes Factors for Forensic Decision Analyses with R provides a self-contained introduction to computational Bayesian statistics using R. With its primary focus on Bayes factors supported by data sets, this book features an operational perspective, practical relevance, and applicability—keeping theoretical and philosophical justifications limited. It offers a balanced approach to three naturally interrelated topics: Probabilistic Inference - Relies on the core concept of Bayesian inferential statistics, to help practicing forensic scientists in the logical and balanced evaluation of the weight of evidence. Decision Making - Features how Bayes factors are interpreted in practical applications to help address questions of decision analysis involving the use of forensic science in the law. Operational Relevance - Combines inference and decision, backed up with practical examples and complete sample code in R, including sensitivity analyses and discussion on how to interpret results in context. Over the past decades, probabilistic methods have established a firm position as a reference approach for the management of uncertainty in virtually all areas of science, including forensic science, with Bayes' theorem providing the fundamental logical tenet for assessing how new information—scientific evidence—ought to be weighed. Central to this approach is the Bayes factor, which clarifies the evidential meaning of new information, by providing a measure of the change in the odds in favor of a proposition of interest, when going from the prior to the posterior distribution. Bayes factors should guide the scientist's thinking about the value of scientific evidence and form the basis of logical and balanced reporting practices, thus representing essential foundations for rational decision making under uncertainty. This book would be relevant to students, practitioners, and applied statisticians interested in inference and decision analyses in the critical field of forensic science. It could be used to support practical courses on Bayesian statistics and decision theory at both undergraduate and graduate levels, and will be of equal interest to forensic scientists and practitioners of Bayesian statistics for driving their evaluations and the use of R for their purposes. This book is Open Access

The Evolution of Cell Communication: The Road not Taken.

In the post-genomic era the complex problem of evolutionary biology can be tackled from the top-down, the bottom-up, or from the middle-out. Given the emergent and contingent nature of this process, we have chosen to take the latter approach, both as a mechanistic link to developmental biology and as a rational means of identifying signaling mechanisms based on their functional genomic significance. Using this approach, we have been able to configure a working model for lung evolution by reverse-engineering lung surfactant from the mammalian lung to the swim bladder of fish. Based on this archetypal cell-molecular model, we have reduced evolutionary biology to cell communication, starting with unicellular organisms communicating with the environment, followed by cell-cell communication to generate metazoa, culminating in the communication of genetic information between generations, i.e. reproduction. This model predicts the evolution of physiologic systems-including development, homeostasis, disease, regeneration/repair, and aging- as a logical consequence of biology reducing entropy. This approach provides a novel and robust way of formulating refutable, testable hypotheses to determine the ultimate origins and first principles of physiology, providing candidate genes for phenotypes hypothesized to have mediated evolutionary changes in structure and/or function. Ultimately, it will form the basis for predictive medicine and molecular bioethics, rather than merely showing associations between genes and pathology, which is an unequivocal Just So Story. In this new age of genomics, our reach must exceed our grasp

Special Libraries, December 1953

Volume 44, Issue 10https://scholarworks.sjsu.edu/sla_sl_1953/1009/thumbnail.jp

Analyses of RNA dynamics in Mus musculus

Ribonucleic acid (RNA) is a biological molecule that exists in the cell of virtually all kinds of known living organisms. Although its significance in fundamental cellular processes such as protein synthesis has been known for decades, an increasing number of novel species and functions of RNA have been discovered recently, and a complete picture of RNA functions in the cell is still elusive. One of the major challenges in RNA studies is that there had been no efficient method to quantify RNA with spatial and temporal information in vivo. In my doctoral studies, I developed a novel RNA sequencing method that metabolically labels RNA in a cell- and time-specific manner in mice and applied this method to solve biological problems that had been difficult to tackle. First, the robustness of the newly developed in vivo RNA labelling method was assessed. To confirm the sensitivity and specificity of RNA labelling, multiple transgenic mice that label RNA in different cell types were generated. By comparing the data obtained from each transgenic strain to previously generated transcriptomic datasets, I confirmed that RNA labelling in a specific cell type was achieved in all the strains analysed. This method would be useful to study cell-type-specific transcriptomics rather than the commonly used laborious and time-intensive cell isolation method often used, and might provide data that closely reflect the native transcriptional state in vivo. Next, using the same RNA labelling method, I tested if there is any RNA that is mobile between different cell types in mice. Intercellular mobility of RNA has been shown in nematodes and plants, but whether there is any RNA that is mobile between different mammalian cells in vivo is still unclear. The cell-specific RNA labelling method allowed us to assess the mobility of RNA directly for the first time. Based on previous publications, three different cell types were chosen as potential “donor” cells, and transgenic mice that label RNA in these cells were generated. The donor cell-derived labelled RNA was sought in “recipient” tissues that are not capable of labelling RNA. However, although RNA labelling was achieved in the donor tissues, no labelled RNA was found in the recipient tissues in any of the animals tested. This experiment presents a novel methodology to assess the mobility of RNA in living mice, and the obtained data suggest that only minor intercellular transfer of RNA, at best, is happening in the tested pairs of tissues. In the final part of my thesis, I applied the metabolic RNA labelling method to study the transcriptional dynamics in the early preimplantation mouse embryos. Unlike conventional RNA sequencing methods that can only quantify RNA abundance in each stage of the embryos, metabolic RNA sequencing can directly interrogate the transcriptional activity of each gene. This method is particularly powerful in studying the transcriptional network in the early preimplantation embryo, where embryo-derived RNA needs to be distinguished from maternally-deposited RNA. By exposing the mouse embryos to a nucleotide analogue in a stage-specific manner, I identified genes that are actively transcribed in the 2-cell embryos. This method would be useful in studying the transcriptional cascade in the early mammalian preimplantation embryos

Professor A.L. Bowley’s theory of the representative method

Arthur. L. Bowley (1869-1957) first advocated the use of surveys--the "representative method"--in 1906 and started to conduct surveys of economic and social conditions in 1912. Bowley's 1926 memorandum for the International Statistical Institute on the "Measurement of the precision attained in sampling" was the first large-scale theoretical treatment of sample surveys as he conducted them. This paper examines Bowley's arguments in the context of the statistical inference theory of the time. The great influence on Bowley's conception of statistical inference was F. Y. Edgeworth but by 1926 R. A. Fisher was on the scene and was attacking Bayesian methods and promoting a replacement of his own. Bowley defended his Bayesian method against Fisher and against Jerzy Neyman when the latter put forward his concept of a confidence interval and applied it to the representative method <br><br> Keywords; history of statistics, sampling theory, bayesian inference