在近代估计理论中出现了一种新的估计方法——Bayes估计，该估计的宗旨是不但要利用本次观测所具有的信息，而且要充分利用观测前我们对参数所具有的信息。本文叙述了用Bayes估计进行方差分量估计的原理，研究了确定方差先验分布的方法，推导了当方差的先验分布为X2分布时方差分量的Bayes估计的具体表达式。最后以一实例说明了Bayes估计的可行性。

One of the typical approaches to linear, inequality-constrained adjustment (LICA) is to solve a least-squares (LS) problem subject to the linear inequality constraints. The main disadvantage of this approach is that the statistical properties of the estimate are not easily determined and thus no general conclusions about the superiority of the estimate can be made. A new approach to solving the LICA problem is proposed. The linear inequality constraints are converted into priorinformation on the parameters with a uniform distribution, and consequently the LICA problem is reformulated into a Bayesian estimation problem. It is shown that the LS estimate of the LICA problem is identical to the Bayesian estimate based on the mode of the posterior distribution. Finally, the Bayesian method is applied to GPS positioning. Results for four field tests show that, when height information is used, the GPS phase ambiguity resolution can be improved significantly and the new approach is feasible.

研究了边坡滑移规律，指出滑移过程为缓慢蠕变→加速蠕变→缓慢蠕变→停止蠕变或缓慢蠕变→加速蠕变→突变→停止蠕变。然后选择了适合于变形分析的非线性回归模型，研究了非线性回归模型的计算方法。并辅以某边坡实测资料分析实例。

首先假设边坡滑坡体为刚体，建立边坡滑坡的动态模型，由此建立卡尔曼滤波的系统方程。边坡的力学状态通过卡尔曼滤波与边坡变形观测的数据联系起来了。模型的不确定性（模型误差）是通过建立的虚拟观测方程来考虑的。与已有的方法不同的是，所建立的方法不仅利用包含在观测中的统计信息，而且能利用边坡滑坡的有关力学状态和地质条件所提供的信息。最后，以一实例论证方法的可行性。