1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
|
Distribution TruncatedDistribution(Normal(mu = 2, sigma = 1.5), bounds = [1, 4])
Elliptical = False
Continuous = True
oneRealization= class=Point name=Unnamed dimension=1 values=[2.9123]
oneSample first= class=Point name=Unnamed dimension=1 values=[1.3426] last= class=Point name=Unnamed dimension=1 values=[3.87446]
mean= class=Point name=Unnamed dimension=1 values=[2.34616]
covariance= class=CovarianceMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[0.63794]
Point= class=Point name=Unnamed dimension=1 values=[1.5]
ddf = class=Point name=Unnamed dimension=1 values=[0.0851881]
ddf (ref)= class=Point name=Unnamed dimension=1 values=[0.0851881]
pdf =0.383346
pdf (ref)=0.383346
cdf=0.178195
ccdf=0.821805
cdf (ref)=0.178195
pdf gradient = class=Point name=Unnamed dimension=4 values=[-0.145716,-0.0590839,0.124394,-0.0638662]
cdf gradient = class=Point name=Unnamed dimension=4 values=[-0.0869866,-0.0104158,-0.266672,-0.0296876]
quantile= class=Point name=Unnamed dimension=1 values=[3.73264]
quantile= class=Point name=Unnamed dimension=1 values=[3.73264]
cdf(quantile)=0.950000
InverseSurvival= class=Point name=Unnamed dimension=1 values=[1.14928]
Survival(inverseSurvival)=0.950000
entropy=1.074411
Minimum volume interval= [1, 3.7326]
threshold= [0.95]
Minimum volume level set= {x | f(x) <= 1.57038} with f=
MinimumVolumeLevelSetEvaluation(TruncatedDistribution(Normal(mu = 2, sigma = 1.5), bounds = [1, 4]))
beta= [0.207966]
Bilateral confidence interval= [1.07578, 3.85894]
beta= [0.95]
Unilateral confidence interval (lower tail)= [1, 3.73264]
beta= [0.95]
Unilateral confidence interval (upper tail)= [1.14928, 4]
beta= [0.95]
mean = class=Point name=Unnamed dimension=1 values=[2.35526]
mean (ref)= class=Point name=Unnamed dimension=1 values=[2.35526]
standard deviation = class=Point name=Unnamed dimension=1 values=[0.802475]
standard deviation (ref)= class=Point name=Unnamed dimension=1 values=[0.802475]
skewness = class=Point name=Unnamed dimension=1 values=[0.190048]
skewness (ref)= class=Point name=Unnamed dimension=1 values=[0.190048]
kurtosis = class=Point name=Unnamed dimension=1 values=[1.9933]
kurtosis (ref)= class=Point name=Unnamed dimension=1 values=[1.9933]
covariance = class=CovarianceMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[0.643966]
covariance (ref)= class=CovarianceMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[0.643966]
parameters = [class=PointWithDescription name=X0 dimension=4 description=[mu_0,sigma_0,lowerBound,upperBound] values=[2,1.5,1,4]]
parameters (ref)= [class=PointWithDescription name=X0 dimension=4 description=[mu,sigma,a,b] values=[2,1.5,1,4]]
parameter = class=Point name=Unnamed dimension=4 values=[2,1.5,1,4]
parameter desc = [mu_0,sigma_0,lowerBound,upperBound]
marginal 0 = class=TruncatedNormal name=TruncatedNormal dimension=1 mu=2 sigma=1.5 a=1 b=4
Standard representative= TruncatedNormal(mu = -0.333333, sigma = 1, a = -1, b = 1)
conditional PDF=0.394038
sequential conditional PDF=[0.394038]
conditional CDF=0.519761
sequential conditional CDF=[0.519761]
Distribution TruncatedDistribution(Normal(mu = 2, sigma = 1.5), bounds = [1, (13.4759) +inf[)
Elliptical = False
Continuous = True
oneRealization= class=Point name=Unnamed dimension=1 values=[2.59149]
oneSample first= class=Point name=Unnamed dimension=1 values=[3.14189] last= class=Point name=Unnamed dimension=1 values=[2.75486]
mean= class=Point name=Unnamed dimension=1 values=[2.6524]
covariance= class=CovarianceMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[1.22045]
Point= class=Point name=Unnamed dimension=1 values=[1.5]
ddf = class=Point name=Unnamed dimension=1 values=[0.0747934]
ddf (ref)= class=Point name=Unnamed dimension=1 values=[0.0747934]
pdf =0.336570
pdf (ref)=0.336570
cdf=0.156452
ccdf=0.843548
cdf (ref)=0.156452
pdf gradient = class=Point name=Unnamed dimension=3 values=[-0.170682,-0.135523,0.0958891]
cdf gradient = class=Point name=Unnamed dimension=3 values=[-0.0962429,-0.0480282,-0.240327]
quantile= class=Point name=Unnamed dimension=1 values=[4.67299]
quantile= class=Point name=Unnamed dimension=1 values=[4.67299]
cdf(quantile)=0.950000
InverseSurvival= class=Point name=Unnamed dimension=1 values=[1.16934]
Survival(inverseSurvival)=0.950000
entropy=1.390942
Minimum volume interval= [1, 4.673]
threshold= [0.95]
Minimum volume level set= {x | f(x) <= 2.62114} with f=
MinimumVolumeLevelSetEvaluation(TruncatedDistribution(Normal(mu = 2, sigma = 1.5), bounds = [1, (13.4759) +inf[))
beta= [0.0727201]
Bilateral confidence interval= [1.08613, 5.12246]
beta= [0.95]
Unilateral confidence interval (lower tail)= [1, 4.67299]
beta= [0.95]
Unilateral confidence interval (upper tail)= [1.16934, 13.4759]
beta= [0.95]
mean = class=Point name=Unnamed dimension=1 values=[2.64103]
mean (ref)= class=Point name=Unnamed dimension=1 values=[2.64103]
standard deviation = class=Point name=Unnamed dimension=1 values=[1.09456]
standard deviation (ref)= class=Point name=Unnamed dimension=1 values=[1.09456]
skewness = class=Point name=Unnamed dimension=1 values=[0.730758]
skewness (ref)= class=Point name=Unnamed dimension=1 values=[0.730758]
kurtosis = class=Point name=Unnamed dimension=1 values=[3.23251]
kurtosis (ref)= class=Point name=Unnamed dimension=1 values=[3.23251]
covariance = class=CovarianceMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[1.19806]
covariance (ref)= class=CovarianceMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[1.19806]
parameters = [class=PointWithDescription name=X0 dimension=3 description=[mu_0,sigma_0,lowerBound] values=[2,1.5,1]]
parameters (ref)= [class=PointWithDescription name=X0 dimension=4 description=[mu,sigma,a,b] values=[2,1.5,1,200]]
parameter = class=Point name=Unnamed dimension=3 values=[2,1.5,1]
parameter desc = [mu_0,sigma_0,lowerBound]
marginal 0 = class=TruncatedNormal name=TruncatedNormal dimension=1 mu=2 sigma=1.5 a=1 b=13.4759
Standard representative= TruncatedNormal(mu = -0.98995, sigma = 0.0150754, a = -1, b = 1)
conditional PDF=0.324748
sequential conditional PDF=[0.324748]
conditional CDF=0.552430
sequential conditional CDF=[0.55243]
Distribution TruncatedDistribution(Normal(mu = 2, sigma = 1.5), bounds = ]-inf (-9.47594), 4])
Elliptical = False
Continuous = True
oneRealization= class=Point name=Unnamed dimension=1 values=[2.45549]
oneSample first= class=Point name=Unnamed dimension=1 values=[0.632917] last= class=Point name=Unnamed dimension=1 values=[0.368086]
mean= class=Point name=Unnamed dimension=1 values=[1.71713]
covariance= class=CovarianceMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[1.67027]
Point= class=Point name=Unnamed dimension=1 values=[1.5]
ddf = class=Point name=Unnamed dimension=1 values=[0.0615199]
ddf (ref)= class=Point name=Unnamed dimension=1 values=[0.0615199]
pdf =0.276840
pdf (ref)=0.276840
cdf=0.406521
ccdf=0.593479
cdf (ref)=0.406521
pdf gradient = class=Point name=Unnamed dimension=3 values=[-0.0282122,-0.119643,-0.0333077]
cdf gradient = class=Point name=Unnamed dimension=3 values=[-0.22793,0.157493,-0.0489101]
quantile= class=Point name=Unnamed dimension=1 values=[3.64324]
quantile= class=Point name=Unnamed dimension=1 values=[3.64324]
cdf(quantile)=0.950000
InverseSurvival= class=Point name=Unnamed dimension=1 values=[-0.53617]
Survival(inverseSurvival)=0.950000
entropy=1.608447
Minimum volume interval= [-0.53617, 4]
threshold= [0.95]
Minimum volume level set= {x | f(x) <= 2.65813} with f=
MinimumVolumeLevelSetEvaluation(TruncatedDistribution(Normal(mu = 2, sigma = 1.5), bounds = ]-inf (-9.47594), 4]))
beta= [0.0700792]
Bilateral confidence interval= [-1.00085, 3.80883]
beta= [0.95]
Unilateral confidence interval (lower tail)= [-9.47594, 3.64324]
beta= [0.95]
Unilateral confidence interval (upper tail)= [-0.53617, 4]
beta= [0.95]
mean = class=Point name=Unnamed dimension=1 values=[1.72929]
mean (ref)= class=Point name=Unnamed dimension=1 values=[1.72929]
standard deviation = class=Point name=Unnamed dimension=1 values=[1.27879]
standard deviation (ref)= class=Point name=Unnamed dimension=1 values=[1.27879]
skewness = class=Point name=Unnamed dimension=1 values=[-0.455767]
skewness (ref)= class=Point name=Unnamed dimension=1 values=[-0.455767]
kurtosis = class=Point name=Unnamed dimension=1 values=[2.84601]
kurtosis (ref)= class=Point name=Unnamed dimension=1 values=[2.84601]
covariance = class=CovarianceMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[1.6353]
covariance (ref)= class=CovarianceMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[1.6353]
parameters = [class=PointWithDescription name=X0 dimension=3 description=[mu_0,sigma_0,upperBound] values=[2,1.5,4]]
parameters (ref)= [class=PointWithDescription name=X0 dimension=4 description=[mu,sigma,a,b] values=[2,1.5,-200,4]]
parameter = class=Point name=Unnamed dimension=3 values=[2,1.5,4]
parameter desc = [mu_0,sigma_0,upperBound]
marginal 0 = class=TruncatedNormal name=TruncatedNormal dimension=1 mu=2 sigma=1.5 a=-9.47594 b=4
Standard representative= TruncatedNormal(mu = 0.980392, sigma = 0.0147059, a = -1, b = 1)
conditional PDF=0.287928
sequential conditional PDF=[0.287928]
conditional CDF=0.471387
sequential conditional CDF=[0.471387]
Distribution TruncatedDistribution(Normal(mu = 2, sigma = 1.5), bounds = [1, 4])
Elliptical = False
Continuous = True
oneRealization= class=Point name=Unnamed dimension=1 values=[3.42681]
oneSample first= class=Point name=Unnamed dimension=1 values=[2.40981] last= class=Point name=Unnamed dimension=1 values=[3.42199]
mean= class=Point name=Unnamed dimension=1 values=[2.35555]
covariance= class=CovarianceMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[0.641586]
Point= class=Point name=Unnamed dimension=1 values=[1.5]
ddf = class=Point name=Unnamed dimension=1 values=[0.0851881]
ddf (ref)= class=Point name=Unnamed dimension=1 values=[0.0851881]
pdf =0.383346
pdf (ref)=0.383346
cdf=0.178195
ccdf=0.821805
cdf (ref)=0.178195
pdf gradient = class=Point name=Unnamed dimension=4 values=[-0.145716,-0.0590839,0.124394,-0.0638662]
cdf gradient = class=Point name=Unnamed dimension=4 values=[-0.0869866,-0.0104158,-0.266672,-0.0296876]
quantile= class=Point name=Unnamed dimension=1 values=[3.73264]
quantile= class=Point name=Unnamed dimension=1 values=[3.73264]
cdf(quantile)=0.950000
InverseSurvival= class=Point name=Unnamed dimension=1 values=[1.14928]
Survival(inverseSurvival)=0.950000
entropy=1.074411
Minimum volume interval= [1, 3.7326]
threshold= [0.95]
Minimum volume level set= {x | f(x) <= 1.57038} with f=
MinimumVolumeLevelSetEvaluation(TruncatedDistribution(Normal(mu = 2, sigma = 1.5), bounds = [1, 4]))
beta= [0.207966]
Bilateral confidence interval= [1.07578, 3.85894]
beta= [0.95]
Unilateral confidence interval (lower tail)= [1, 3.73264]
beta= [0.95]
Unilateral confidence interval (upper tail)= [1.14928, 4]
beta= [0.95]
mean = class=Point name=Unnamed dimension=1 values=[2.35526]
mean (ref)= class=Point name=Unnamed dimension=1 values=[2.35526]
standard deviation = class=Point name=Unnamed dimension=1 values=[0.802475]
standard deviation (ref)= class=Point name=Unnamed dimension=1 values=[0.802475]
skewness = class=Point name=Unnamed dimension=1 values=[0.190048]
skewness (ref)= class=Point name=Unnamed dimension=1 values=[0.190048]
kurtosis = class=Point name=Unnamed dimension=1 values=[1.9933]
kurtosis (ref)= class=Point name=Unnamed dimension=1 values=[1.9933]
covariance = class=CovarianceMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[0.643966]
covariance (ref)= class=CovarianceMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[0.643966]
parameters = [class=PointWithDescription name=X0 dimension=4 description=[mu_0,sigma_0,lowerBound,upperBound] values=[2,1.5,1,4]]
parameters (ref)= [class=PointWithDescription name=X0 dimension=4 description=[mu,sigma,a,b] values=[2,1.5,1,4]]
parameter = class=Point name=Unnamed dimension=4 values=[2,1.5,1,4]
parameter desc = [mu_0,sigma_0,lowerBound,upperBound]
marginal 0 = class=TruncatedNormal name=TruncatedNormal dimension=1 mu=2 sigma=1.5 a=1 b=4
Standard representative= TruncatedNormal(mu = -0.333333, sigma = 1, a = -1, b = 1)
conditional PDF=0.394038
sequential conditional PDF=[0.394038]
conditional CDF=0.519761
sequential conditional CDF=[0.519761]
Distribution TruncatedDistribution(Normal(mu = [0,0], sigma = [1,1], R = [[ 1 0 ]
[ 0 1 ]]), bounds = [-0.5, 2]
[-0.5, 2])
Elliptical = False
Continuous = True
oneRealization= class=Point name=Unnamed dimension=2 values=[0.394328,0.761261]
oneSample first= class=Point name=Unnamed dimension=2 values=[-0.255554,0.136819] last= class=Point name=Unnamed dimension=2 values=[1.95356,-0.095957]
mean= class=Point name=Unnamed dimension=2 values=[0.440783,0.449352]
covariance= class=CovarianceMatrix dimension=2 implementation=class=MatrixImplementation name=Unnamed rows=2 columns=2 values=[0.375687,0.000808423,0.000808423,0.37764]
Point= class=Point name=Unnamed dimension=2 values=[1.5,1.5]
ddf = class=Point name=Unnamed dimension=2 values=[-0.0562691,-0.0562691]
ddf (ref)= class=Point name=Unnamed dimension=2 values=[-0.0251622,-0.0251622]
pdf =0.037513
pdf (ref)=0.016775
cdf=0.872574
ccdf=0.127426
cdf (ref)=0.870849
pdf gradient = class=Point name=Unnamed dimension=9 values=[0.039548,0.0628233,0.039548,0.0628233,0.0844037,0.0197498,0.0197498,-0.00302873,-0.00302873]
cdf gradient = class=Point name=Unnamed dimension=9 values=[-0.07807,-0.146682,-0.07807,-0.146682,0,-0.0324012,-0.0324012,-0.0704505,-0.0704505]
quantile= class=Point name=Unnamed dimension=2 values=[1.75438,1.75438]
quantile= class=Point name=Unnamed dimension=2 values=[1.95451,1.95451]
cdf(quantile)=0.950000
InverseSurvival= class=Point name=Unnamed dimension=2 values=[-0.452458,-0.452458]
Survival(inverseSurvival)=0.950000
entropy=1.608356
mean = class=Point name=Unnamed dimension=2 values=[0.445744,0.445744]
mean (ref)= class=Point name=Unnamed dimension=2 values=[0,0]
standard deviation = class=Point name=Unnamed dimension=2 values=[0.613672,0.613672]
standard deviation (ref)= class=Point name=Unnamed dimension=2 values=[1,1]
skewness = class=Point name=Unnamed dimension=2 values=[0.467305,0.467305]
skewness (ref)= class=Point name=Unnamed dimension=2 values=[0,0]
kurtosis = class=Point name=Unnamed dimension=2 values=[2.34902,2.34902]
kurtosis (ref)= class=Point name=Unnamed dimension=2 values=[3,3]
covariance = class=CovarianceMatrix dimension=2 implementation=class=MatrixImplementation name=Unnamed rows=2 columns=2 values=[0.376594,0,0,0.376594]
covariance (ref)= class=CovarianceMatrix dimension=2 implementation=class=MatrixImplementation name=Unnamed rows=2 columns=2 values=[1,0,0,1]
parameters = [class=PointWithDescription name=X0 dimension=9 description=[mu_0,sigma_0,mu_1,sigma_1,R_1_0,lowerBound_0,lowerBound_1,upperBound_0,upperBound_1] values=[0,1,0,1,0,-0.5,-0.5,2,2]]
parameters (ref)= [class=PointWithDescription name=X0 dimension=2 description=[mu_0,sigma_0] values=[0,1],class=PointWithDescription name=X1 dimension=2 description=[mu_1,sigma_1] values=[0,1],class=PointWithDescription name=dependence dimension=1 description=[R_1_0] values=[0]]
parameter = class=Point name=Unnamed dimension=9 values=[0,1,0,1,0,-0.5,-0.5,2,2]
parameter desc = [mu_0,sigma_0,mu_1,sigma_1,R_1_0,lowerBound_0,lowerBound_1,upperBound_0,upperBound_1]
marginal 0 = class=TruncatedDistribution name=TruncatedDistribution distribution=class=Normal name=Normal dimension=1 mean=class=Point name=Unnamed dimension=1 values=[0] sigma=class=Point name=Unnamed dimension=1 values=[1] correlationMatrix=class=CorrelationMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[1] bounds=class=Interval name=Unnamed dimension=1 lower bound=class=Point name=Unnamed dimension=1 values=[-0.5] upper bound=class=Point name=Unnamed dimension=1 values=[2] finite lower bound=[1] finite upper bound=[1] thresholdRealization=0.5
Standard representative= Normal(mu = [0,0], sigma = [1,1], R = [[ 1 0 ]
[ 0 1 ]])
conditional PDF=0.540165
sequential conditional PDF=[0.540165,0.540165]
conditional CDF=0.543689
sequential conditional CDF=[0.543689,0.543689]
Distribution TruncatedDistribution(WeibullMin(beta = 2, alpha = 3, gamma = 0), bounds = [0, (6.36519) +inf[)
Elliptical = False
Continuous = True
oneRealization= class=Point name=Unnamed dimension=1 values=[2.23016]
oneSample first= class=Point name=Unnamed dimension=1 values=[0.847165] last= class=Point name=Unnamed dimension=1 values=[2.09176]
mean= class=Point name=Unnamed dimension=1 values=[1.78206]
covariance= class=CovarianceMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[0.41974]
Point= class=Point name=Unnamed dimension=1 values=[1.5]
ddf = class=Point name=Unnamed dimension=1 values=[0.270908]
ddf (ref)= class=Point name=Unnamed dimension=1 values=[0.270908]
pdf =0.553345
pdf (ref)=0.553345
cdf=0.344184
ccdf=0.655816
cdf (ref)=0.344184
pdf gradient = class=Point name=Unnamed dimension=4 values=[-0.479854,0.0924181,-0.270908,0]
cdf gradient = class=Point name=Unnamed dimension=4 values=[-0.415009,-0.0795937,-0.553345,0]
quantile= class=Point name=Unnamed dimension=1 values=[2.88313]
quantile= class=Point name=Unnamed dimension=1 values=[2.88313]
cdf(quantile)=0.950000
InverseSurvival= class=Point name=Unnamed dimension=1 values=[0.743105]
Survival(inverseSurvival)=0.950000
entropy=0.979345
Minimum volume interval= [0.53717, 3.0308]
threshold= [0.95]
Minimum volume level set= {x | f(x) <= 2.24308} with f=
MinimumVolumeLevelSetEvaluation(TruncatedDistribution(WeibullMin(beta = 2, alpha = 3, gamma = 0), bounds = [0, (6.36519) +inf[))
beta= [0.106131]
Bilateral confidence interval= [0.587271, 3.09026]
beta= [0.95]
Unilateral confidence interval (lower tail)= [0, 2.88313]
beta= [0.95]
Unilateral confidence interval (upper tail)= [0.743105, 6.36519]
beta= [0.95]
mean = class=Point name=Unnamed dimension=1 values=[1.78596]
mean (ref)= class=Point name=Unnamed dimension=1 values=[1.78596]
standard deviation = class=Point name=Unnamed dimension=1 values=[0.649101]
standard deviation (ref)= class=Point name=Unnamed dimension=1 values=[0.649101]
skewness = class=Point name=Unnamed dimension=1 values=[0.168103]
skewness (ref)= class=Point name=Unnamed dimension=1 values=[0.168103]
kurtosis = class=Point name=Unnamed dimension=1 values=[2.72946]
kurtosis (ref)= class=Point name=Unnamed dimension=1 values=[2.72946]
covariance = class=CovarianceMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[0.421332]
covariance (ref)= class=CovarianceMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[0.421332]
parameters = [class=PointWithDescription name=X0 dimension=4 description=[beta,alpha,gamma,lowerBound] values=[2,3,0,0]]
parameters (ref)= [class=PointWithDescription name=X0 dimension=3 description=[beta,alpha,gamma] values=[2,3,0]]
parameter = class=Point name=Unnamed dimension=4 values=[2,3,0,0]
parameter desc = [beta,alpha,gamma,lowerBound]
marginal 0 = class=WeibullMin name=WeibullMin dimension=1 beta=2 alpha=3 gamma=0
Standard representative= WeibullMin(beta = 1, alpha = 3, gamma = 0)
conditional PDF=0.586847
sequential conditional PDF=[0.586847]
conditional CDF=0.509374
sequential conditional CDF=[0.509374]
d= TruncatedDistribution(Normal(mu = 1, sigma = 2), bounds = [-1, 4]) simplified= TruncatedNormal(mu = 1, sigma = 2, a = -1, b = 4)
d= TruncatedDistribution(Uniform(a = 1, b = 2), bounds = [0.2, 2.4]) simplified= Uniform(a = 1, b = 2)
d= TruncatedDistribution(Exponential(lambda = 1, gamma = 2), bounds = [2.5, 65]) simplified= Exponential(lambda = 1, gamma = 2.5)
d= TruncatedDistribution(TruncatedDistribution(WeibullMin(beta = 1, alpha = 1, gamma = 0), bounds = [1.5, 7.8]), bounds = [2.5, 6]) simplified= TruncatedDistribution(WeibullMin(beta = 1, alpha = 1, gamma = 0), bounds = [2.5, 6])
d= TruncatedDistribution(Beta(alpha = 1.5, beta = 6.3, a = -1, b = 2), bounds = [-2.5, 6]) simplified= Beta(alpha = 1.5, beta = 6.3, a = -1, b = 2)
d= TruncatedDistribution(JointDistribution(Normal(mu = 0, sigma = 1), Normal(mu = 0, sigma = 1), IndependentCopula(dimension = 2)), bounds = [0, 1]
[0, 1]) simplified= JointDistribution(TruncatedDistribution(Normal(mu = 0, sigma = 1), bounds = [0, 1]), TruncatedDistribution(Normal(mu = 0, sigma = 1), bounds = [0, 1]), IndependentCopula(dimension = 2))
d= TruncatedDistribution(BlockIndependentDistribution(Normal(mu = [0,0], sigma = [1,1], R = [[ 1 0 ]
[ 0 1 ]]), Normal(mu = [0,0], sigma = [1,1], R = [[ 1 0 ]
[ 0 1 ]])), bounds = [0, 1]
[0, 1]
[0, 1]
[0, 1]) simplified= BlockIndependentDistribution(TruncatedDistribution(Normal(mu = [0,0], sigma = [1,1], R = [[ 1 0 ]
[ 0 1 ]]), bounds = [0, 1]
[0, 1]), TruncatedDistribution(Normal(mu = [0,0], sigma = [1,1], R = [[ 1 0 ]
[ 0 1 ]]), bounds = [0, 1]
[0, 1]))
d= TruncatedDistribution(BlockIndependentCopula(NormalCopula(R = [[ 1 0 ]
[ 0 1 ]]), NormalCopula(R = [[ 1 0 ]
[ 0 1 ]])), bounds = [0, 1]
[0, 1]
[0, 1]
[0, 1]) simplified= BlockIndependentDistribution(TruncatedDistribution(NormalCopula(R = [[ 1 0 ]
[ 0 1 ]]), bounds = [0, 1]
[0, 1]), TruncatedDistribution(NormalCopula(R = [[ 1 0 ]
[ 0 1 ]]), bounds = [0, 1]
[0, 1]))
d= TruncatedDistribution(Dirichlet(theta = [0.7,0.3]), bounds = [0.2, 2.4]) simplified= Beta(alpha = 0.7, beta = 0.3, a = 0.2, b = 1)
after setbounds q@0.9= [29.628]
proba=0.001382
q@0.1= [0.425]
|
