Accurate decomposition of methanol maser spectra is essential for understanding high-mass star-forming regions, especially in complex blended spectra where small differences alter physical interpretation. Conventional Gaussian fitting often fails to capture non-Gaussian structure and lacks uncertainty quantification. We develop a Bayesian spectral decomposition framework using Gaussian, Lorentzian, and Voigt profiles with Markov Chain Monte Carlo sampling, enabling model comparison and uncertainty estimation. Applied to the 6.7\,GHz methanol maser G339.884$-$1.259 observed with the Ghana Radio Astronomy Observatory, our method reveals seven velocity-coherent components. The Voigt model is statistically preferred, yielding the lowest AIC and BIC ($\approx 1.98 \times 10^{4}$ and $1.99 \times 10^{4}$), the smallest RMSE ($\approx 11.1$ Jy), and the highest $R^{2}$ (0.985). Purely Gaussian or Lorentzian models leave systematic residuals. Elevated reduced $χ^{2}_ν$ values indicate unresolved substructure and non-ideal noise. Bayesian inference provides a robust framework for maser spectral analysis, extendable to other molecular lines and combinable with high-resolution interferometry.

翻译：甲醇脉泽谱线的精确分解对于理解大质量恒星形成区至关重要，尤其是在复杂混叠谱线中，微小差异会改变物理诠释。传统高斯拟合往往无法捕捉非高斯结构，且缺乏不确定性量化。我们开发了基于马尔可夫链蒙特卡洛采样的贝叶斯谱线分解框架，采用高斯、洛伦兹和沃伊特轮廓进行模型比较与不确定性估计。将该方法应用于加纳射电天文台观测的6.7 GHz甲醇脉泽G339.884$-$1.259，揭示出七个速度相干成分。沃伊特模型在统计上具有最优性：其AIC和BIC值最低（分别约为$1.98 \times 10^{4}$和$1.99 \times 10^{4}$），均方根误差最小（约11.1 Jy），拟合优度$R^{2}$最高（0.985）。纯高斯或洛伦兹模型则残留系统误差。较高的约化$\chi^{2}_ν$值表明存在未解析的精细结构及非理想噪声。贝叶斯推断为脉泽谱线分析提供了稳健框架，可推广至其他分子谱线并与高分辨率干涉测量相结合。