# br Proving It is assumed that the

Proving. It is assumed that Exendin-4 the calculation form of probability density in measurement matrix Aul and silk is:

2σw
i

yqi −Aulk

i

πσ

The calculation model for posteriori distribution of analysis model for the use of anesthetic analgesic drugs in cancer patients can be obtained based on Bayesian rule:
p(silk
yqi , Aulk
) =
p(yqi Aulk , silk )p(silk
)
(5)

The Fourier marginal likelihood constant is adopted in the for-mula, with the main function to normalize the posteriori distribu-tion of analysis model for the use of anesthetic analgesic drugs in cancer patients. The calculation form is as follows:

Formula (6) is substituted into Formula (5) to replace the pos-terior probability parameter in model, and the improvement form can be obtained as follows:
p(silk yqi , Aulk ) ∝ exp

sil
wi ulk

sil
)

µ

−1
Aulk yqi

sil
wi
(
wi
Aulk
+ Ci0
)

It is shown that the posterior distribution parameter of the analysis model for the use of anesthetic analgesic drugs in cancer patients based on Gaussian function is calculated as follows:

σwi

In the formula:

For the parameters σwi and Ci0 adopted in the analysis model for the use of anesthetic analgesic drugs in cancer patients, the mean value of p(silk yqi , Aulk ) obtained from the maximum estimate of posterior probability can be calculated as follows:
µ
=
(σw
−1
T
Aulk
)−1
T
yqi

ˆ sil

Aulk

Justified!

analysis model for the use of anesthetic
analgesic drugs in cancer

patients, the fault model in analysis subnet for the use of anesthetic analgesic drugs in cancer patients is expanded in the form as below:

µsxl
µsyl

µszl

E

log p(si

silk
yqi
;θ

The parameter σwi , γi,

i in the analysis model for the use of
The value of parameter γi,

ishall be considered synchronously

anesthetic analgesic drugs in cancer patients is solved through
in diagnostic model and the Formula (20) can be simplified as

optimizing the expected limit value and the calculation results
follows:

of p

i

can be obtained. Such calculation

mode shows that i in model calculation can be converted through

log p(sil

Esi

yqi

x, y or z. This kind of process mode can reduce − log p

lk

k

to the greatest degree and the likelihood boundary of analysis

(
(
))
Meanwhile, because the following relation is established:

data for the use of anesthetic analgesic drugs in cancer patients is

calculated as follows:

T

k

γii

sil

k

log

log

k

yqi

L

yqi

i

i

yi

yi

where,
yi = σwIM
+ Aulk Ci0 AuTlk . The iterative computations

of
parameters σ
wi
, γ
,
i
in the analysis model for the use of

i

∑
anesthetic analgesic drugs in cancer patients can be characterized by adopting the following theorem.
Then the equivalent form of Formula (21) is:
Q (γi, θ(t)) ∝ − 2
log γi −
2 log

i
k

k

i

Theorem 2. The learning process for the following rules is executed
∑
for the parameters σwi , γi, i in the analysis model for the use of anesthetic analgesic drugs in cancer patients by using the maximum likelihood estimation.
σwi

yqi − Aulk silk

M

µsil (µsil
)

k

γi

T

where, the estimated results of parameter items µsil and Ci can be obtained based on the calculation process shown in Formula (11).
The training calculation form for parameter item γican be obtained through acquiring partial derivatives as follows:
γi

T

k

In a similar way, passive transport is projected that
(∑i) (m
,

established. The theorem is justified.

Proving. The maximum likelihood estimation algorithm consists

of two major processes during the execution: E estimation and M
Moreover, in order to improve the diagnostic performance of

estimation.

analysis model for the use of anesthetic analgesic drugs in cancer

(1) E estimation: the mean value of likelihood function in the

analysis model for the use of anesthetic analgesic drugs in cancer
patients, the improvement measure adopted is to define the value

patients shall be calculated through expectation function:

of
i and adopt the symmetric model structure. The component

i

element of matrix is

Then the model of
nonzero elements in matrix shall be constructed.