当我尝试编译 .tex 文件时，MikTeX 会冻结，即使是之前编译过的文件也是如此。我使用 WinEdt 作为文本编辑器。我试图实现的是：
\documentclass[11pt]{report}
\usepackage{bm}
\usepackage[english]{babel}
\usepackage{amsmath}
\usepackage{geometry}
\geometry{left=1in,right=1in,top=1in,bottom=1in}
% ----------------------------------------------------------------
\begin{document}
\subsection*{MCMC Estimation}
One of the drawbacks to the MCMC methodology I had proposed is the long runtime, particularly when there are large numbers of items and mixture components. This concern led to the introduction of alternative methodologies, such as Fast Approximate Bayes and Weighted Likelihood Bootstrap, which I explain below. Given that the computer I use had eight core processors, I explored the possibility of using parallel processing to speed up the MCMC process. MATLAB informed me that parallel processing was not possible with the MCMC code as written. I translated the MCMC code into R, but was unable to write code that would perform parallel processing. Consequently, I took a "poor man's" approach, and ran $M=5$ independent MCMC chains in MATLAB. The running of independent chains reminded me of the $\hat{R}$ convergence test that Gelman, Carlin, Stern, and Rubin (GCSR; 2004). $\hat{R}$ is an ANOVA-like statistic that compares the between-chain variance of draws from the posterior distribution with the variance within chains of these draws. Let $\bm{\phi}_s$ denote the draw of the vector of parameters for draw $s=\{1,\dots,S\}$ of $S$ MCMC iterations. After every 10,000 posterior draws (iterations), let $N$ denote the number of draws retained in the second half of the MCMC simulations. Let $\bm{\psi}_{ms}$ denote the $s$-th iteration's vector of posterior draws from batch $m$. In addition, let $\bm{\psi}_{m\cdot}$ and $\bm{\psi}_{\cdot\cdot}$, respectively, denote batch $m$'s posterior mean and the (overall) mean from all batches combined. Then, $B$, the between-batch variance is given by
$$
B = \frac{N}{M-1} (\bm{\psi}_{m\cdot} - \bm{\psi}_{\cdot\cdot})^\prime (\bm{\psi}_{m\cdot} - \bm{\psi}_{\cdot\cdot}),
$$
while $W$, the mean within-batch variance is
$$
W = \frac{1}{M}\sum_{m=1}^M s^2_m,
$$
where $s^2_m$ denotes the variance of batch $m$:
$$
s^2_m = \frac{1}{N-1} (\bm{\psi}_{ms} - \bm{\psi}_{m\cdot})^\prime (\bm{\psi}_{ms} - \bm{\psi}_{m\cdot}).
$$
The estimated marginal posterior variance, $\widehat{\text{var}}(\bm{\psi}|\text{Y})$, conditional on the observed data, $Y$, is a weighted average of the between- and within-batch variances. That is,
$$
\widehat{\text{var}}(\bm{\psi}|\text{Y}) = \frac{(N-1)W + B}{N}.
$$
WinEdt 无法完全处理 .tex 文档。WinEdt 给我的日志文件如下所示：
Command Line: pdflatex.exe --interaction=errorstopmode --synctex=-1 "DissertationResearchSummaryFall2019.tex"
Startup Folder: C:\Users\Ethan Arenson\Google Drive\Research\Dissertation
This is pdfTeX, Version 3.14159265-2.6-1.40.20 (MiKTeX 2.9.7250 64-bit)
entering extended mode
(DissertationResearchSummaryFall2019.tex
LaTeX2e <2019-10-01> patch level 3
("C:\Program Files\MiKTeX 2.9\tex/latex/base\report.cls"
Document Class: report 2019/10/25 v1.4k Standard LaTeX document class
("C:\Program Files\MiKTeX 2.9\tex/latex/base\size11.clo"))
("C:\Program Files\MiKTeX 2.9\tex/latex/tools\bm.sty")
("C:\Program Files\MiKTeX 2.9\tex/generic/babel\babel.sty"
("C:\Program Files\MiKTeX 2.9\tex/generic/babel\switch.def")
======================================================================
.
.
.
("C:\Program Files\MiKTeX 2.9\tex/latex/arabi\bblopts.cfg")
("C:\Program Files\MiKTeX 2.9\tex/latex/babel-english\english.ldf"
("C:\Program Files\MiKTeX 2.9\tex/generic/babel\babel.def"
.
.
.
(DissertationResearchSummaryFall2019.aux)
*geometry* driver: auto-detecting
*geometry* detected driver: pdftex
)
*
WinEdt 就到此为止了。
感谢@Bernard，我明白了。不知从何时起，我失去了我的
\end{document}
命令。