Literary analysis Quotes

“Class analysis can thus function not simply as part of scientific theory of interests and conflicts, but of an emancipatory theory of alternatives and social justice as well. Even if socialism is off the historical agenda, the idea of countering the exploitative logic of capitalism is not.”

“Class analysis can thus function not simply as part of scientific theory of interests and conflicts, but of an emancipatory theory of alternatives and social justice as well. Even if socialism is off the historical agenda, the idea of countering the exploitative logic of capitalism is not.”

“Analysis does not transform consciousness.”

“Reflecting on various aspects of our lives is essential for a person to grow and adjust to changing phases in their life. Self-analysis entails examining a person’s existing level of self-esteem and documenting the inner voice that speaks to a person, which is frequently either affirming of self-defeating. Failure to periodically engage in self-analysis, make crucial revisions in our personas, and modify our thinking patterns when we encounter transformative events in life can lead to mood disorders, burnout, and other emotional maladies.”

“Too much knowledge and analysis can be paralysis.”

“Truth suffers from too much analysis.”

“Analysis is the art of creation through destruction.”

“All business sagacity reduces itself in the last analysis to judicious use of sabotage.”

“Apply analysis when appropriate, but keep it on a short leash when joy beckons.”

“In the last analysis, our only freedom is the freedom to discipline ourselves.”

“Due to the various pragmatic obstacles, it is rare for a mission-critical analysis to be done in the “fully Bayesian” manner, i.e., without the use of tried-and-true frequentist tools at the various stages. Philosophy and beauty aside, the reliability and efficiency of the underlying computations required by the Bayesian framework are the main practical issues. A central technical issue at the heart of this is that it is much easier to do optimization (reliably and efficiently) in high dimensions than it is to do integration in high dimensions. Thus the workhorse machine learning methods, while there are ongoing efforts to adapt them to Bayesian framework, are almost all rooted in frequentist methods. A work-around is to perform MAP inference, which is optimization based.Most users of Bayesian estimation methods, in practice, are likely to use a mix of Bayesian and frequentist tools. The reverse is also true—frequentist data analysts, even if they stay formally within the frequentist framework, are often influenced by “Bayesian thinking,” referring to “priors” and “posteriors.” The most advisable position is probably to know both paradigms well, in order to make informed judgments about which tools to apply in which situations.”