Multivariate and Multichannel Discrete Hidden Markov Models for Categorical Sequences
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Updated
Oct 3, 2024 - R
Multivariate and Multichannel Discrete Hidden Markov Models for Categorical Sequences
HMM-integrated Bayesian approach for detecting CNV and LOH events from single-cell RNA-seq data
Analysis of subclonal copy number alterations (CNA) and loss of heterozygosity (LOH) in cancer
Full Bayesian Inference for Hidden Markov Models
Travel time prediction from GPS observations using an HMM
Estimation of natural selection and allele age from time series allele frequency data using a novel likelihood-based approach
A stochastic epidemiological model that supplements the conventional reported cases with pooled samples from wastewater for assessing the overall SARS-CoV-2 burden at the community level.
A R-Shiny web interface that forecasts fuel prices based on historical data, using HMM.
An HMM-based domain caller from bw
Estimating temporally variable selection intensity from ancient DNA data with a combination of forward- and backward-in-time simulations
A local TU annotation tool for multi-sample comparison
Generalized Pair Hidden Markov Chain Model (GPHMM)
Estimating temporally variable selection intensity from ancient DNA data
Project to explore state space model inference including Kalman filters and hidden Markov models.
Estimate flight tracks from radio-telemetry data using a Hidden Markov Model.
2023/24 Frühjahrssemester Statistical Models in Computational Biology @ ETHz
Estimating temporally variable selection intensity from ancient DNA data with the flexibility of modelling linkage and epistasis
Detecting and quantifying natural selection at two linked loci from time series data of allele frequencies with forward-in-time simulations
The objective is to build a predictive model which is able to distinguish between our main product categories.
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