2. GENERALIZATION OF RELIABILITY CONCEPT 
The originally defind reliability only describes the ability of a 
photogrammetric system to detect gross and systematic errors with 
the aid of a statistical test. Now, in order that the reliability 
concept can be Spoken about both to a system and to a result,and 
not only in the case of using data-snooping but also in that of 
using robust methodS, we need to generalize the concept of 
reliability as follows: 
Reliability is concerned with the bias, or the difference of the 
Statistical expectation of an estimator from the true value of the 
estimated quantity. The reliability of a System describes the 
ability of the system to avoid biases. While the reliability of a 
result describes the biases contained in the concrete result. The 
reliability referring to the unnecessary unknowns, Such as the 
additional parameters, is called internal reliability and that to 
the necessary unknowns or their required functions called external 
reliability. 
This generalization of the reliability concept is rather natural. 
No contradiction to the original meaning of reliability has been 
made. The original reliability is just the reliability of a system. 
While the measure to be proposed below iS a measure of the 
reliability of a result. In the context gross and systematic 
errors are both considered as the errors in the functional model 
only, that is, as the biases of estimators.only. 
3. POSTERIORI RELIABILITY IN THE CASE ADOPTING DATA-SNOOPING 
Suppose that the following linear functional model and stochastical 
model are true for observation vector 1 (nx1): 
E(1) 
Ax. Hs (1) 
D(1) 
g2% | (2) 
where x is a vector of main unknowns (m,x1), 
S is a vector of additional parameters (mox1), 
A is a design matrix (nxm,) with the rank being m, , 
H is a coefficient matrix (nxm, , I^ £ n-m4). 
Because estimation using the complete model (1) does not necessarily 
lead to good estimators of the main unknowns in the sense of mean 
Square error, some procedure of parameter selection is usually 
carried out and the complete model is revised thereby for estimation 
/^/. However biases are thus induced in the result of adjustment. 
Suppose that the complete model (1) is revised by deleting 
additional parameter 8; . Then the least Squares estimators of the 
unknowns by the revised model are biased if the true value of sj 
is not equal to zero. In fact, the deleted parameter S, takes zero 
as its estimator. So the bias for s, is just the true value of S4 
itself. This bias is passed to the estimators of other parameters. 
Thus the biases for the parameterS are all relative to the true 
value of S, . From section 2 we know evaluating those biases is 
required to assess the reliability of the result. 
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