Fiducial probability

1967 ◽  
Vol 8 (2) ◽  
pp. 99-109 ◽  
Author(s):  
J. G. Kalbfleisch ◽  
D. A. Sprott
Keyword(s):  
Fiducial Probability

Determination of the pH of Hemolyzed Packed Red Cells from Arterial Blood

1961 ◽  
Vol 7 (5) ◽  
pp. 536-541 ◽  
Author(s):  
May K Purcell ◽  
Gertrude M Still ◽  
Theodore Rodman ◽  
Henry P Close
Keyword(s):  
Blood Cell ◽  
Normal Subjects ◽  
The Body ◽  
Red Cell ◽  
Arterial Blood ◽  
Fiducial Probability ◽  
The Mean ◽  
Red Cell Ph

Abstract A technic is described for the determination of the in vivo pH of red blood cell hemolysates. The mean arterial red cell pH of 20 normal subjects was 7.19 with a range of 7.15 to 7.22. The fiducial probability at the 0.95 level is 7.13 to 7.25. The mean difference in pH between plasma and cells was 0.21, with a range of 0.15 to 0.23. It is suggested that changes in pH of erythrocytes may reflect changes in other less accessible cells of the body and that the determination may be a useful research and clinical procedure in the study of metabolic and respiratory derangements.

Age Reporting of Very Old Samples

Radiocarbon ◽  
1980 ◽  
Vol 22 (4) ◽  
pp. 1021-1027 ◽  
Author(s):  
Adam Walanus ◽  
Mieczysław F Pazdur
Keyword(s):  
Probability Density Function ◽  
Probability Distribution ◽  
Probability Density ◽  
Density Function ◽  
Statistical Interpretation ◽  
Very Old ◽  
Radiocarbon Age ◽  
Fiducial Probability ◽  
Limiting Value

Problems of the statistical interpretation of radiocarbon age measurements of old samples are discussed, based on the notion of fiducial probability distribution. A probability density function of age has been given. A detailed discussion of different facets of the probability distribution of age has led us to the confirmation of the use of 2σ as the best limiting value between the regions of finite and infinite dates. It has been proposed to make use of the principle of constant probability P = 0.68 in the regions of both finite and infinite ages instead of the criterion N + kσ.

scholarly journals The concepts of inverse probability and fiducial probability referring to unknown parameters

1933 ◽  
Vol 139 (838) ◽  
pp. 343-348 ◽  
Keyword(s):  
Standard Deviation ◽  
Practical Application ◽  
Unknown Parameters ◽  
Inverse Probability ◽  
Fiducial Probability ◽  
The Mean ◽  
Normally Distributed

In a paper published in these 'Proceedings' Jeffreys puts forward a form of reasoning purporting to resolve in a particular case the primitive difficulty which besets all attempts to derive valid results of practical application from the theory of Inverse Probability. For a normally distributed variate, x , the frequency element may be written df = h /√π e - h 2 ( x - μ ) 2 dx , where μ is the mean of the distribution, and h the precision constant. For the convenience of the majority of statisticians who prefer to use the standard deviation, σ, of the distribution, in place of the precision constant, we may note that h 2 = 1/2σ 2 , and that this substitution may be made at any stage of the argument.

Inverse probability and sufficient statistics

1949 ◽  
Vol 45 (2) ◽  
pp. 225-229
Author(s):  
V. S. Huzurbazar
Keyword(s):  
Probability Density Function ◽  
Probability Density ◽  
Density Function ◽  
Statistical Estimation ◽  
Sufficient Statistics ◽  
Inverse Probability ◽  
General Terms ◽  
Fiducial Probability ◽  
Posterior Probability Density ◽  
Posterior Probability Density Function

1. It is an interesting fact that in many problems of statistical estimation the results given by the theory of inverse probability (as modified by Jeffreys) are indistinguishable from those given by the methods of ‘fiducial probability’ or ‘confidence intervals’. The derivation of some of the important inverse distributions by Jeffreys(3) arouses one's curiosity. It seems that when this agreement is noticed there are usually sufficient statistics for parameters in the distribution. The object of this note is to throw some light, in general terms, on the similarity in form between the posterior probability-density function of the parameters and the probability-density function of the distribution when it admits sufficient statistics. For convenience the following notation in Jeffreys's probability logic is used below:P(q | p) is the probability of a proposition q on data p.

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