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Bayesian data analysis
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This book provides a clear exposition of the underlying concepts with many worked examples and problem sets. It also discusses implementation, including an introduction to Markov chain Monte-Carlo integration and linear and nonlinear model fitting. Particularly extensive coverage of spectral analysis (detecting and measuring periodic signals) includes a self-contained introduction to Fourier and discrete Fourier methods.
489p
vimeyers
29-05-2024
2
2
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This study employs the Bayesian Model Averaging (BMA) algorithm to evaluate input variable importance and select the most reliable salinity prediction model. Based on an analysis of observed salinity data and climate data extracted from Landsat 8 OLI in the Google Earth Engine platform, the BMA algorithm identifies the significance of critical variables and optimal salinity prediction models.
15p
viohoyo
25-04-2024
3
2
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Continued part 1, part 2 of ebook "Statistics and data analysis for financial engineering with R examples" provides readers with contents including: time series models basics; time series models further topics; GARCH models; cointegration; portfolio selection; the capital asset pricing model; factor models and principal components; risk management; bayesian data analysis and MCMC; nonparametric regression and splines;...
407p
thamnhuocgiai
24-09-2023
7
4
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Part 1 book "Reliability and risks - A bayesian perspective" includes content: Uction and overview; the quantification of uncertainty, exchangeability and indifference, stochastic models of failure, parametric failure data analysis.
205p
oursky01
24-07-2023
6
4
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Measurements of single-cell methylation are revolutionizing our understanding of epigenetic control of gene expression, yet the intrinsic data sparsity limits the scope for quantitative analysis of such data.
15p
vigalileogalilei
27-02-2022
8
1
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Multiplexed single-cell RNA-seq analysis of multiple samples using pooling is a promising experimental design, offering increased throughput while allowing to overcome batch variation. To reconstruct the sample identify of each cell, genetic variants that segregate between the samples in the pool have been proposed as natural barcode for cell demultiplexing.
12p
vielonmusk
30-01-2022
8
0
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Allele-specific measurements of transcription factor binding from ChIP-seq data are key to dissecting the allelic effects of non-coding variants and their contribution to phenotypic diversity. However, most methods of detecting an allelic imbalance assume diploid genomes.
17p
vialfrednobel
29-01-2022
10
0
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The structure and mechanical properties of chromatin impact DNA functions and nuclear architecture but remain poorly understood. In budding yeast, a simple polymer model with minimal sequence-specific constraints and a small number of structural parameters can explain diverse experimental data on nuclear architecture.
15p
vialfrednobel
29-01-2022
13
0
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Various efforts have been made to elucidate the cooperating proteins involved in maintaining chromatin interactions; however, many are still unknown. Here, we present 3CPET, a tool based on a non-parametric Bayesian approach, to infer the set of the most probable protein complexes involved in maintaining chromatin interactions and the regions that they may control, making it a valuable downstream analysis tool in chromatin conformation studies.
16p
viaristotle
29-01-2022
10
0
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We present a generative model, Lux, to quantify DNA methylation modifications from any combination of bisulfite sequencing approaches, including reduced, oxidative, TET-assisted, chemical-modification assisted, and methylaseassisted bisulfite sequencing data.
22p
viaristotle
29-01-2022
7
0
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Using whole genome sequence (WGS) data, we assess two methods for detecting mixed infection: (i) a combination of the number of heterozygous sites and the proportion of heterozygous sites to total SNPs, and (ii) Bayesian model-based clustering of allele frequencies from sequencing reads at heterozygous sites.
15p
vitzuyu2711
29-09-2021
7
1
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Hi-C and capture Hi-C (CHi-C) are used to map physical contacts between chromatin regions in cell nuclei using high-throughput sequencing. Analysis typically proceeds considering the evidence for contacts between each possible pair of fragments independent from other pairs.
13p
vigiselle2711
30-08-2021
8
1
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One of the major challenges in microbial studies is detecting associations between microbial communities and a specific disease. A specialized feature of microbiome count data is that intestinal bacterial communities form clusters called as “enterotype”, which are characterized by differences in specific bacterial taxa, making it difficult to analyze these data under health and disease conditions.
13p
visilicon2711
20-08-2021
10
1
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In previous studies, the prior probability has been considered as a fixed value only, hence, the Bayes error is usually a fixed value. This sometimes leads to unreasonable results. To fill the mentioned research gap, this paper considers the prior probability q in Bayesian classifier as a distribution, and looks insight the posterior distribution of Bayes error, using Monte-Carlo simulation. Finally, the proposed method is applied to credit scoring data of a bank in Vietnam. Based on the results, we can determine whether the Bayesian classifier is suitable for data or not.
10p
angicungduoc9
04-01-2021
10
1
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RNA-seq, a massive parallel-sequencing-based transcriptome profiling method, provides digital data in the form of aligned sequence read counts. The comparative analyses of the data require appropriate statistical methods to estimate the differential expression of transcript variants across different cell/tissue types and disease conditions.
12p
viwyoming2711
16-12-2020
13
0
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Testing for marginal associations between numerous genetic variants and disease may miss complex relationships among variables (e.g., gene-gene interactions). Bayesian approaches can model multiple variables together and offer advantages over conventional model building strategies, including using existing biological evidence as modeling priors and acknowledging that many models may fit the data well.
10p
viwyoming2711
16-12-2020
10
1
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Accurate genomic variant detection is an essential step in gleaning medically useful information from genome data. However, low concordance among variant-calling methods reduces confidence in the clinical validity of whole genome and exome sequence data, and confounds downstream analysis for applications in genome medicine.
12p
vikentucky2711
26-11-2020
12
1
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Complex designs are common in (observational) clinical studies. Sequencing data for such studies are produced more and more often, implying challenges for the analysis, such as excess of zeros, presence of random effects and multi-parameter inference.
7p
vikentucky2711
26-11-2020
9
1
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Mass spectrometry-based metabolomic analysis depends upon the identification of spectral peaks by their mass and retention time. Statistical analysis that follows the identification currently relies on one main peak of each compound.
11p
vikentucky2711
26-11-2020
15
1
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With recent development in sequencing technology, a large number of genome-wide DNA methylation studies have generated massive amounts of bisulfite sequencing data. The analysis of DNA methylation patterns helps researchers understand epigenetic regulatory mechanisms.
12p
vikentucky2711
26-11-2020
17
1
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