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fiducial    
a. 基准的,根据信仰的,信托的

基准的,根据信仰的,信托的

fiducial
adj 1: relating to or of the nature of a legal trust (i.e. the
holding of something in trust for another); "a fiduciary
contract"; "in a fiduciary capacity"; "fiducial power"
[synonym: {fiduciary}, {fiducial}]
2: used as a fixed standard of reference for comparison or
measurement; "a fiducial point"
3: based on trust

Fiducial \Fi*du"cial\, a. [L. fiducia trust, confidence; akin to
fides faith. See {Faith}.]
1. Having faith or trust; confident; undoubting; firm.
"Fiducial reliance on the promises of God." --Hammond.
[1913 Webster]

2. Having the nature of a trust; fiduciary; as, fiducial
power. --Spelman.
[1913 Webster]

{Fiducial edge} (Astron. & Surv.), the straight edge of the
alidade or ruler along which a straight line is to be
drawn.

{Fiducial line} or {Fiducial point} (Math. & Physics.), a
line or point of reference, as for setting a graduated
circle or scale used for measurements.
[1913 Webster]


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  • What does fiducial mean (in the context of statistics)?
    26 When I Google for "fisher" "fiducial" I sure get a lot of hits, but all the ones I've followed are utterly beyond my comprehension All these hits do seem to have one thing in common: they are all written for dyed-in-the-wool statisticians, people thoroughly steeped in the theory, practice, history, and lore of statistics
  • What is the fiducial argument and why has it not been accepted?
    One of the late contributions of R A Fisher was fiducial intervals and fiducial principled arguments This approach however is nowhere near as popular as frequentist or Bayesian principled argumen
  • Multiple comparisons with p-value corrections of fiducial limits?
    We originally discussed differences based on non-overlapping fiducial limits, but a reviewer requested formal testing with correction for multiple comparisons How'd you address this request?
  • Understanding the Behrens–Fisher problem - Cross Validated
    That is what led to fiducial inference A Link to the Savage article The Biography by Fisher's daughter Joan Fisher Box R A Fisher An Appreciation, Hinkley and Feinberg editors A book by Erich Lehmann about Fisher and Neyman and the birth of Classical Statistics This is a link to an earlier post that I commented on that you also posted
  • Fiducial Inference in Machine Learning - Cross Validated
    I was looking at the Fiducial Inference page on wikipedia, which is an alternative to the traditional Frequentist and Bayesian standpoints Although it was out of favour in mainstream statistics fo
  • Whats the difference between a confidence interval and a credible . . .
    Joris and Srikant user28's exchange here got me wondering (again) if my internal explanations for the difference between confidence intervals and credible intervals were the correct ones How you
  • How to calculate a confidence level for a Poisson distribution?
    You'll need to complete a few actions and gain 15 reputation points before being able to upvote Upvoting indicates when questions and answers are useful What's reputation and how do I get it? Instead, you can save this post to reference later
  • How to derive the standard error of linear regression coefficient
    another way of thinking about the n-2 df is that it's because we use 2 means to estimate the slope coefficient (the mean of Y and X) df from Wikipedia: " In general, the degrees of freedom of an estimate of a parameter are equal to the number of independent scores that go into the estimate minus the number of parameters used as intermediate steps in the estimation of the parameter itself "
  • Compare 90th percentiles of two samples (confidence interval, test)
    Interpret the fiducial distributions as probabilitiy distributions (this is an approximation, the fiducial distribution does not behave exactly like a probability distribution) and compute the probabilities for the joint probabilities of the two parameters percentiles to be inside the 2-d bins cells created by the grid
  • Doing maximum p-value estimation instead of maximum likelihood
    Whenever we do maximum likelihood estimation, we look for the parameters that maximize the probability density of the data On the other hand, when we compute p-values, we look at the tail probabil





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