Believing Probabilistic Contents: On the Expressive Power and Coherence of Sets of Sets of Probabilities

Moss (2018) argues that rational agents are best thought of not as having degrees of belief in various propositions but as having beliefs in probabilistic contents, or probabilistic beliefs. Probabilistic contents are sets of probability functions. Probabilistic belief states, in turn, are modeled by sets of probabilistic contents, or sets of sets of probability functions. We argue that this Mossean framework is of considerable interest quite independently of its role in Moss’ account of probabilistic knowledge or her semantics for epistemic modals and probability operators. It is an extremely general model of uncertainty. Indeed, it is at least as general and expressively powerful as every other current imprecise probability framework, including lower probabilities, lower previsions, sets of probabilities, sets of desirable gambles, and choice functions. In addition, we partially answer an important question that Moss leaves open, viz., why should rational agents have consistent probabilistic beliefs? We show that an important subclass of Mossean believers avoid Dutch bookability iff they have consistent probabilistic beliefs

Spectral gene set enrichment (SGSE)

Motivation: Gene set testing is typically performed in a supervised context to quantify the association between groups of genes and a clinical phenotype. In many cases, however, a gene set-based interpretation of genomic data is desired in the absence of a phenotype variable. Although methods exist for unsupervised gene set testing, they predominantly compute enrichment relative to clusters of the genomic variables with performance strongly dependent on the clustering algorithm and number of clusters. Results: We propose a novel method, spectral gene set enrichment (SGSE), for unsupervised competitive testing of the association between gene sets and empirical data sources. SGSE first computes the statistical association between gene sets and principal components (PCs) using our principal component gene set enrichment (PCGSE) method. The overall statistical association between each gene set and the spectral structure of the data is then computed by combining the PC-level p-values using the weighted Z-method with weights set to the PC variance scaled by Tracey-Widom test p-values. Using simulated data, we show that the SGSE algorithm can accurately recover spectral features from noisy data. To illustrate the utility of our method on real data, we demonstrate the superior performance of the SGSE method relative to standard cluster-based techniques for testing the association between MSigDB gene sets and the variance structure of microarray gene expression data. Availability: http://cran.r-project.org/web/packages/PCGSE/index.html Contact: [email protected] or [email protected]

The Graphic Design of Jason Moore

Artist Statement As a Graphic Designer the goal of most of my work is to relay information to the viewer. I try to insert as much of myself into each work that I design and draw influence from Designers such as Michael Schwab, Massimo Vignelli and David Carson. Using typography and illustration to convey emotion and personality. My art evokes a light and airy mood through the use of minimal color, negative space and geometric shapes. The mood of my work is reflected by my personality, easy going and kind, the work is reacted to visually by the viewer in the same manner, its easy to look at and isn\u27t imposing to the viewer. I want the viewer to take something away from the work without having to force it down their throat and use stark typography to assist in this.https://digitalcommons.murraystate.edu/art399/1023/thumbnail.jp

Principal component gene set enrichment (PCGSE)

Motivation: Although principal component analysis (PCA) is widely used for the dimensional reduction of biomedical data, interpretation of PCA results remains daunting. Most existing methods attempt to explain each principal component (PC) in terms of a small number of variables by generating approximate PCs with few non-zero loadings. Although useful when just a few variables dominate the population PCs, these methods are often inadequate for characterizing the PCs of high-dimensional genomic data. For genomic data, reproducible and biologically meaningful PC interpretation requires methods based on the combined signal of functionally related sets of genes. While gene set testing methods have been widely used in supervised settings to quantify the association of groups of genes with clinical outcomes, these methods have seen only limited application for testing the enrichment of gene sets relative to sample PCs. Results: We describe a novel approach, principal component gene set enrichment (PCGSE), for computing the statistical association between gene sets and the PCs of genomic data. The PCGSE method performs a two-stage competitive gene set test using the correlation between each gene and each PC as the gene-level test statistic with flexible choice of both the gene set test statistic and the method used to compute the null distribution of the gene set statistic. Using simulated data with simulated gene sets and real gene expression data with curated gene sets, we demonstrate that biologically meaningful and computationally efficient results can be obtained from a simple parametric version of the PCGSE method that performs a correlation-adjusted two-sample t-test between the gene-level test statistics for gene set members and genes not in the set. Availability: http://cran.r-project.org/web/packages/PCGSE/index.html Contact: [email protected] or [email protected]

Zenvertigo

The way I design is visually representative of the way I understand the world. Clean typography and strong layout are accented by texture, line and illustration to become understandable. There is no “because I said so” in my work, it all has a purpose and is meant to be calm, clear, and conscious of the viewer. To me rock climbing and graphic design are similar, both require focus, accuracy and awareness. The architecture of an artificial wall is geometric and modern unlike the organic age-old beauty of a natural cliff. Zenvertigo is a climbing gym based in Anchorage, Alaska that visually merges the outdoor with the indoor by using modern typography and clean lines with organic textures and images. I used pieces of imagery around Anchorage to build the foundation of the brand; navy blue mimics the midnight sky, light greens reference Chugach State Park, round neon-style type signals the Aurora Borealis and the complex texture comes from the granite that creates Mount Denali. The logo is a response to the feeling of vertigo, moving vertically while being signaled directionally by the chevron symbol. David Carson’s experimental typography, strong dominant type from the late great Massimo Vignelli, and the incorporation of personality by Stefan Sagmeister helped to shape the foundation of the brand.https://digitalcommons.murraystate.edu/art498/1037/thumbnail.jp