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Aisaku Arakawa1, Takeshi Hayashi2, Masaaki Taniguchi1

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Summary
This summary is machine-generated.

Hamiltonian Monte Carlo (HMC) sampling improves parameter estimation in animal breeding. Optimal tuning of HMC’s leapfrog integration enhances its performance over Gibbs sampling for genetic and genomic models.

Keywords:
Gibbs samplingHamiltonian Monte Carlogenomic selectionleapfrog integrationmixed model

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Area of Science:

  • Computational Biology
  • Statistical Genetics
  • Machine Learning

Background:

  • Markov chain Monte Carlo (MCMC) methods are crucial for parameter estimation in complex models.
  • Hamiltonian Monte Carlo (HMC) offers potential improvements in sampling efficiency.
  • Effective application of HMC requires careful tuning of its numerical integration parameters.

Purpose of the Study:

  • To optimize the leapfrog integration parameters for Hamiltonian Monte Carlo (HMC) in animal breeding data analysis.
  • To evaluate the performance of HMC with optimal tunings against traditional methods like Gibbs sampling.
  • To demonstrate the utility of HMC in models incorporating pedigree and genomic information.

Main Methods:

  • Applied Hamiltonian Monte Carlo (HMC) to animal breeding data.
  • Identified optimal leapfrog integration parameters (time steps, stepsize) for normal and inverse chi-square distributions.
  • Utilized HMC in statistical models for variance explained by pedigree and genomic information.
  • Compared HMC performance with Gibbs sampling.

Main Results:

  • Optimal leapfrog integration tunings were identified for HMC.
  • HMC demonstrated superior performance compared to Gibbs sampling in both pedigree and genomic models.
  • The study provides insights into HMC's effectiveness for quantitative genetics.

Conclusions:

  • Hamiltonian Monte Carlo (HMC) provides a more effective parameter estimation method for animal breeding than Gibbs sampling.
  • Optimal tuning of HMC’s leapfrog integration is critical for achieving superior performance.
  • The study validates HMC's utility in modern animal genetics, offering efficient analysis of large datasets.