First part shows several methods to sample points from arbitrary distributions. Second part shows application to population genetics to infer population size and divergence time using obtained sequence data.
1. The document discusses probabilistic modeling and variational inference. It introduces concepts like Bayes' rule, marginalization, and conditioning.
2. An equation for the evidence lower bound is derived, which decomposes the log likelihood of data into the Kullback-Leibler divergence between an approximate and true posterior plus an expected log likelihood term.
3. Variational autoencoders are discussed, where the approximate posterior is parameterized by a neural network and optimized to maximize the evidence lower bound. Latent variables are modeled as Gaussian distributions.
First part shows several methods to sample points from arbitrary distributions. Second part shows application to population genetics to infer population size and divergence time using obtained sequence data.
1. The document discusses probabilistic modeling and variational inference. It introduces concepts like Bayes' rule, marginalization, and conditioning.
2. An equation for the evidence lower bound is derived, which decomposes the log likelihood of data into the Kullback-Leibler divergence between an approximate and true posterior plus an expected log likelihood term.
3. Variational autoencoders are discussed, where the approximate posterior is parameterized by a neural network and optimized to maximize the evidence lower bound. Latent variables are modeled as Gaussian distributions.
Trinity outperforms Newbler 2.5 in de novo assembly of 454 transcriptome data. Trinity assembles more contigs with longer N50 and total bases. It also reconstructs more full-length transcripts matching the usagi CDS reference sequences compared to Newbler. However, Trinity also assembles more poly-A/T sequences which may reflect non-coding RNA.
The document discusses genome sequencing data analysis. It mentions using TopHat and Cufflinks software to analyze NG-Seq data from genomeDB and NCBI SRA. It also discusses sorting transcript expression data and accessing gene information from databases like MGI and UCSC.
The document discusses using R with interactive design and Ruby on Rails. It provides examples of using RSRuby to integrate R functions and graphics with Ruby code. This allows R output like histograms and graphs to be generated from within Ruby on Rails applications and displayed on web pages. It also describes how R can be used for data analysis and visualization independently or with tools like TIBCO Spotfire and Excel.
This document discusses analyzing biomedical literature using R and summarizes statistics on terms from PubMed. It shows how to import a CSV file of PubMed records into R, filter the data by year, and calculate sums of term frequencies. Over 19 million records are in PubMed with over 111,000 containing terms like Southern blotting, Northern blotting, and Western blotting. R can be used to efficiently analyze large text corpora from sources like PubMed.
The document appears to be a menu listing options for statistical software including R, ESS, help menus, and demos. It includes options for R and ESS statistical packages, help menus, and demos as well as listings for Bible software.
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