Likelihood, Bayesian and MCMC methods in quantitative genetics:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
New York
Springer-Verlag
2002
|
| Schriftenreihe: | Statistics for biology and health
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | Includes bibliographical references (p. [701]-726) and index |
| Beschreibung: | XVII, 740 S. Ill. |
| ISBN: | 0387954406 |
Internformat
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| 100 | 1 | |a Sorensen, Daniel |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Likelihood, Bayesian and MCMC methods in quantitative genetics |c Daniel Sorensen, Daniel Gianola |
| 264 | 1 | |a New York |b Springer-Verlag |c 2002 | |
| 300 | |a XVII, 740 S. |b Ill. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 490 | 0 | |a Statistics for biology and health | |
| 500 | |a Includes bibliographical references (p. [701]-726) and index | ||
| 650 | 7 | |a Genetica |2 gtt | |
| 650 | 7 | |a Statistische methoden |2 gtt | |
| 650 | 4 | |a Genetics -- Statistical methods | |
| 650 | 4 | |a Monte Carlo method | |
| 650 | 4 | |a Markov processes | |
| 650 | 4 | |a Bayesian statistical decision theory | |
| 650 | 0 | 7 | |a Genetik |0 (DE-588)4071711-2 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Statistik |0 (DE-588)4056995-0 |2 gnd |9 rswk-swf |
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| 689 | 0 | 1 | |a Genetik |0 (DE-588)4071711-2 |D s |
| 689 | 0 | |5 DE-604 | |
| 700 | 1 | |a Gianola, Daniel |e Sonstige |4 oth | |
| 856 | 4 | 2 | |m DNB Datenaustausch |q application/pdf |u http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=009847898&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |3 Inhaltsverzeichnis |
| 943 | 1 | |a oai:aleph.bib-bvb.de:BVB01-009847898 | |
Datensatz im Suchindex
| _version_ | 1808226434179661824 |
|---|---|
| adam_text |
CONTENTS
PREFACE
V
I
REVIEW
OF
PROBABILITY
AND
DISTRIBUTION
THEORY
1
1
PROBABILITY
AND
RANDOM
VARIABLES
3
1.1
INTRODUCTION
.
3
1.2
UNIVARIATE
DISCRETE
DISTRIBUTIONS
.
4
1.2.1
THE
BERNOULLI
AND
BINOMIAL
DISTRIBUTIONS
.
7
1.2.2
THE
POISSON
DISTRIBUTION
.
10
1.2.3
BINOMIAL
DISTRIBUTION:
NORMAL
APPROXIMATION
.
12
1.3
UNIVARIATE
CONTINUOUS
DISTRIBUTIONS
.
13
1.3.1
THE
UNIFORM,
BETA,
GAMMA,
NORMAL,
AND
STUDENT-T
DISTRIBUTIONS
.
18
1.4
MULTIVARIATE
PROBABILITY
DISTRIBUTIONS
.
29
1.4.1
THE
MULTINOMIAL
DISTRIBUTION
.
37
1.4.2
THE
DIRICHLET
DISTRIBUTION
.
40
1.4.3
THE
D-DIMENSIONAL
UNIFORM
DISTRIBUTION
.
40
1.4.4
THE
MULTIVARIATE
NORMAL
DISTRIBUTION
.
41
1.4.5
THE
CHI-SQUARE
DISTRIBUTION
.
53
1.4.6
THE
WISHART
AND
INVERSE
WISHART
DISTRIBUTIONS
.
55
1.4.7
THE
MULTIVARIATE-T
DISTRIBUTION
.
60
1.5
DISTRIBUTIONS
WITH
CONSTRAINED
SAMPLE
SPACE
.
62
1.6
ITERATED
EXPECTATIONS
.
67
XII
CONTENTS
2
FUNCTIONS
OF
RANDOM
VARIABLES
77
2.1
INTRODUCTION
.
77
2.2
FUNCTIONS
OF
A
SINGLE
RANDOM
VARIABLE
.
78
2.2.1
DISCRETE
RANDOM
VARIABLES
.
78
2.2.2
CONTINUOUS
RANDOM
VARIABLES
.
79
2.2.3
APPROXIMATING
THE
MEAN
AND
VARIANCE
.
89
2.2.4
DELTA
METHOD
.
93
2.3
FUNCTIONS
OF
SEVERAL
RANDOM
VARIABLES
.
95
2.3.1
LINEAR
TRANSFORMATIONS
.
ILL
2.3.2
APPROXIMATING
THE
MEAN
AND
COVARIANCE
MATRIX
.
.
114
II
METHODS
OF
INFERENCE
117
3
AN
INTRODUCTION
TO
LIKELIHOOD
INFERENCE
119
3.1
INTRODUCTION
.
119
3.2
THE
LIKELIHOOD
FUNCTION
.
120
3.3
THE
MAXIMUM
LIKELIHOOD
ESTIMATOR
.
122
3.4
LIKELIHOOD
INFERENCE
IN
A
GAUSSIAN
MODEL
.
125
3.5
FISHER
'
S
INFORMATION
MEASURE
.
128
3.5.1
SINGLE
PARAMETER
CASE
.
128
3.5.2
ALTERNATIVE
REPRESENTATION
OF
INFORMATION
.
131
3.5.3
MEAN
AND
VARIANCE
OF
THE
SCORE
FUNCTION
.
134
3.5.4
MULTIPARAMETER
CASE
.
135
3.5.5
CRAMER-RAO
LOWER
BOUND
.
138
3.6
SUFFICIENCY
.
142
3.7
ASYMPTOTIC
PROPERTIES:
SINGLE
PARAMETER
MODELS
.
143
3.7.1
PROBABILITY
OF
THE
DATA
GIVEN
THE
PARAMETER
.
144
3.7.2
CONSISTENCY
.
146
3.7.3
ASYMPTOTIC
NORMALITY
AND
EFFICIENCY
.
147
3.8
ASYMPTOTIC
PROPERTIES:
MULTIPARAMETER
MODELS
.
152
3.9
FUNCTIONAL
INVARIANCE
.
153
3.9.1
ILLUSTRATION
OF
FUNCTIONAL
INVARIANCE
.
153
3.9.2
INVARIANCE
IN
A
SINGLE
PARAMETER
MODEL
.
157
3.9.3
INVARIANCE
IN
A
MULTIPARAMETER
MODEL
.
159
4
FURTHER
TOPICS
IN
LIKELIHOOD
INFERENCE
161
4.1
INTRODUCTION
.
161
4.2
COMPUTATION
OF
MAXIMUM
LIKELIHOOD
ESTIMATES
.
162
4.3
EVALUATION
OF
HYPOTHESES
.
166
4.3.1
LIKELIHOOD
RATIO
TESTS
.
166
4.3.2
CONFIDENCE
REGIONS
.
177
4.3.3
WALD
'
S
TEST
.
179
4.3.4
SCORE
TEST
.
179
4.4
NUISANCE
PARAMETERS
.
181
CONTENTS
XIII
4.4.1
LOSS
OF
EFFICIENCY
DUE
TO
NUISANCE
PARAMETERS
.
.
.
182
4.4.2
MARGINAL
LIKELIHOODS
.
182
4.4.3
PROFILE
LIKELIHOODS
.
186
4.5
ANALYSIS
OF
A
MULTINOMIAL
DISTRIBUTION
.
190
4.5.1
AMOUNT
OF
INFORMATION
PER
OBSERVATION
.
199
4.6
ANALYSIS
OF
LINEAR
LOGISTIC
MODELS
.
202
4.6.1
THE
LOGISTIC
DISTRIBUTION
.
204
4.6.2
LIKELIHOOD
FUNCTION
UNDER
BERNOULLI
SAMPLING
.
.
.
204
4.6.3
MIXED
EFFECTS
LINEAR
LOGISTIC
MODEL
.
208
5
AN
INTRODUCTION
TO
BAYESIAN
INFERENCE
211
5.1
INTRODUCTION
.
211
5.2
BAYES
THEOREM:
DISCRETE
CASE
.
214
5.3
BAYES
THEOREM:
CONTINUOUS
CASE
.
223
5.4
POSTERIOR
DISTRIBUTIONS
.
235
5.5
BAYESIAN
UPDATING
.
249
5.6
FEATURES
OF
POSTERIOR
DISTRIBUTIONS
.
257
5.6.1
POSTERIOR
PROBABILITIES
.
258
5.6.2
POSTERIOR
QUANTILES
.
262
5.6.3
POSTERIOR
MODES
.
264
5.6.4
POSTERIOR
MEAN
VECTOR
AND
COVARIANCE
MATRIX
.
.
.
280
6
BAYESIAN
ANALYSIS
OF
LINEAR
MODELS
287
6.1
INTRODUCTION
.
287
6.2
THE
LINEAR
REGRESSION
MODEL
.
287
6.2.1
INFERENCE
UNDER
UNIFORM
IMPROPER
PRIORS
.
288
6.2.2
INFERENCE
UNDER
CONJUGATE
PRIORS
.
297
6.2.3
ORTHOGONAL
PARAMETERIZATION
OF
THE
MODEL
.
307
6.3
THE
MIXED
LINEAR
MODEL
.
313
6.3.1
BAYESIAN
VIEW
OF
THE
MIXED
EFFECTS
MODEL
.
313
6.3.2
JOINT
AND
CONDITIONAL
POSTERIOR
DISTRIBUTIONS
.
317
6.3.3
MARGINAL
DISTRIBUTION
OF
VARIANCE
COMPONENTS
.
.
.
322
6.3.4
MARGINAL
DISTRIBUTION
OF
LOCATION
PARAMETERS
.
323
7
THE
PRIOR
DISTRIBUTION
AND
BAYESIAN
ANALYSIS
327
7.1
INTRODUCTION
.
327
7.2
AN
ILLUSTRATION
OF
THE
EFFECT
OF
PRIORS
ON
INFERENCES
.
328
7.3
A
RAPID
TOUR
OF
BAYESIAN
ASYMPTOTICS
.
330
7.3.1
DISCRETE
PARAMETER
.
330
7.3.2
CONTINUOUS
PARAMETER
.
331
7.4
STATISTICAL
INFORMATION
AND
ENTROPY
.
334
7.4.1
INFORMATION
.
334
7.4.2
ENTROPY
OF
A
DISCRETE
DISTRIBUTION
.
337
7.4.3
ENTROPY
OF
A
JOINT
AND
CONDITIONAL
DISTRIBUTION
.
.
340
7.4.4
ENTROPY
OF
A
CONTINUOUS
DISTRIBUTION
.
341
XIV
CONTENTS
7.4.5
INFORMATION
ABOUT
A
PARAMETER
.
346
7.4.6
FISHER
'
S
INFORMATION
REVISITED
.
351
7.4.7
PRIOR
AND
POSTERIOR
DISCREPANCY
.
353
7.5
PRIORS
CONVEYING
LITTLE
INFORMATION
.
356
7.5.1
THE
UNIFORM
PRIOR
.
356
7.5.2
OTHER
VAGUE
PRIORS
.
358
7.5.3
MAXIMUM
ENTROPY
PRIOR
DISTRIBUTIONS
.
367
7.5.4
REFERENCE
PRIOR
DISTRIBUTIONS
.
379
8
BAYESIAN
ASSESSMENT
OF
HYPOTHESES
AND
MODELS
399
8.1
INTRODUCTION
.
399
8.2
BAYES
FACTORS
.
400
8.2.1
DEFINITION
.
400
8.2.2
INTERPRETATION
.
402
8.2.3
THE
BAYES
FACTOR
AND
HYPOTHESIS
TESTING
.
403
8.2.4
INFLUENCE
OF
THE
PRIOR
DISTRIBUTION
.
412
8.2.5
NESTED
MODELS
.
414
8.2.6
APPROXIMATIONS
TO
THE
BAYES
FACTOR
.
418
8.2.7
PARTIAL
AND
INTRINSIC
BAYES
FACTORS
.
422
8.3
ESTIMATING
THE
MARGINAL
LIKELIHOOD
.
424
8.4
GOODNESS
OF
FIT
AND
MODEL
COMPLEXITY
.
429
8.5
GOODNESS
OF
FIT
AND
PREDICTIVE
ABILITY
OF
A
MODEL
.
433
8.5.1
ANALYSIS
OF
RESIDUALS
.
434
8.5.2
PREDICTIVE
ABILITY
AND
PREDICTIVE
CROSS-VALIDATION
.
436
8.6
BAYESIAN
MODEL
AVERAGING
.
439
8.6.1
GENERAL
.
439
8.6.2
DEFINITIONS
.
440
8.6.3
PREDICTIVE
ABILITY
OF
BMA
.
441
9
APPROXIMATE
INFERENCE
VIA
THE
EM
ALGORITHM
443
9.1
INTRODUCTION
.
443
9.2
COMPLETE
AND
INCOMPLETE
DATA
.
444
9.3
THE
EM
ALGORITHM
.
445
9.3.1
FORM
OF
THE
ALGORITHM
.
445
9.3.2
DERIVATION
.
445
9.4
MONOTONIC
INCREASE
OF
HIP
(0|Y)
.
447
9.5
THE
MISSING
INFORMATION
PRINCIPLE
.
448
9.5.1
COMPLETE,
OBSERVED
AND
MISSING
INFORMATION
.
448
9.5.2
RATE
OF
CONVERGENCE
OF
THE
EM
ALGORITHM
.
449
9.6
EM
THEORY
FOR
EXPONENTIAL
FAMILIES
.
451
9.7
STANDARD
ERRORS
AND
POSTERIOR
STANDARD
DEVIATIONS
.
452
9.7.1
THE
METHOD
OF
LOUIS
.
453
9.7.2
SUPPLEMENTED
EM
ALGORITHM
(SEM)
.
454
9.7.3
THE
METHOD
OF
OAKES
.
457
9.8
EXAMPLES
.
458
CONTENTS
XV
III
MARKOV
CHAIN
MONTE
CARLO
METHODS
475
10
AN
OVERVIEW
OF
DISCRETE
MARKOV
CHAINS
477
10.1
INTRODUCTION
.
477
10.2
DEFINITIONS
.
478
10.3
STATE
OF
THE
SYSTEM
AFTER
N-STEPS
.
479
10.4
LONG-TERM
BEHAVIOR
OF
THE
MARKOV
CHAIN
.
481
10.5
STATIONARY
DISTRIBUTION
.
481
10.6
APERIODICITY
AND
IRREDUCIBILITY
.
483
10.7
REVERSIBLE
MARKOV
CHAINS
.
487
10.8
LIMITING
BEHAVIOR
.
492
11
MARKOV
CHAIN
MONTE
CARLO
497
11.1
INTRODUCTION
.
497
11.2
PRELIMINARIES
.
498
11.2.1
NOTATION
.
498
11.2.2
TRANSITION
KERNELS
.
499
11.2.3
VARYING
DIMENSIONALITY
.
499
11.3
AN
OVERVIEW
OF
MARKOV
CHAIN
MONTE
CARLO
.
500
11.4
THE
METROPOLIS-HASTINGS
ALGORITHM
.
502
11.4.1
AN
INFORMAL
DERIVATION
.
502
11.4.2
A
MORE
FORMAL
DERIVATION
.
504
11.5
THE
GIBBS
SAMPLER
.
509
11.5.1
FULLY
CONDITIONAL
POSTERIOR
DISTRIBUTIONS
.
510
11.5.2
THE
GIBBS
SAMPLING
ALGORITHM
.
510
11.6
LANGEVIN-HASTINGS
ALGORITHM
.
517
11.7
REVERSIBLE
JUMP
MCMC
.
517
11.7.1
THE
INVARIANT
DISTRIBUTION
.
518
11.7.2
GENERATING
THE
PROPOSAL
.
519
11.7.3
SPECIFYING
THE
REVERSIBILITY
CONDITION
.
520
11.7.4
DERIVATION
OF
THE
ACCEPTANCE
PROBABILITY
.
522
11.7.5
DETERMINISTIC
PROPOSALS
.
523
11.7.6
GENERATING
PROPOSALS
VIA
THE
IDENTITY
MAPPING
.
.
.
525
11.8
DATA
AUGMENTATION
.
532
12
IMPLEMENTATION
AND
ANALYSIS
OF
MCMC
SAMPLES
539
12.1
INTRODUCTION
.
539
12.2
A
SINGLE
LONG
CHAIN
OR
SEVERAL
SHORT
CHAINS?
.
540
12.3
CONVERGENCE
ISSUES
.
541
12.3.1
EFFECT
OF
POSTERIOR
CORRELATION
ON
CONVERGENCE
.
.
.
541
12.3.2
MONITORING
CONVERGENCE
.
547
12.4
INFERENCES
FROM
THE
MCMC
OUTPUT
.
550
12.4.1
ESTIMATORS
OF
POSTERIOR
QUANTITIES
.
550
12.4.2
MONTE
CARLO
VARIANCE
.
553
12.5
SENSITIVITY
ANALYSIS
.
556
XVI
CONTENTS
IV
APPLICATIONS
IN
QUANTITATIVE
GENETICS
561
13
GAUSSIAN
AND
THICK-TAILED
LINEAR
MODELS
563
13.1
INTRODUCTION
.
563
13.2
THE
UNIVARIATE
LINEAR
ADDITIVE
GENETIC
MODEL
.
564
13.2.1
A
GIBBS
SAMPLING
ALGORITHM
.
566
13.3
ADDITIVE
GENETIC
MODEL
WITH
MATERNAL
EFFECTS
.
570
13.3.1
FULLY
CONDITIONAL
POSTERIOR
DISTRIBUTIONS
.
575
13.4
THE
MULTIVARIATE
LINEAR
ADDITIVE
GENETIC
MODEL
.
576
13.4.1
FULLY
CONDITIONAL
POSTERIOR
DISTRIBUTIONS
.
580
13.5
A
BLOCKED
GIBBS
SAMPLER
FOR
GAUSSIAN
LINEAR
MODELS
.
.
.
584
13.6
LINEAR
MODELS
WITH
THICK-TAILED
DISTRIBUTIONS
.
588
13.6.1
MOTIVATION
.
588
13.6.2
A
STUDENT-T
MIXED
EFFECTS
MODEL
.
595
13.6.3
MODEL
WITH
CLUSTERED
RANDOM
EFFECTS
.
600
13.7
PARAMETERIZATIONS
AND
THE
GIBBS
SAMPLER
.
602
14
THRESHOLD
MODELS
FOR
CATEGORICAL
RESPONSES
605
14.1
INTRODUCTION
.
605
14.2
ANALYSIS
OF
A
SINGLE
POLYCHOTOMOUS
TRAIT
.
607
14.2.1
SAMPLING
MODEL
.
607
14.2.2
PRIOR
DISTRIBUTION
AND
JOINT
POSTERIOR
DENSITY
.
.
.
608
14.2.3
FULLY
CONDITIONAL
POSTERIOR
DISTRIBUTIONS
.
611
14.2.4
THE
GIBBS
SAMPLER
.
615
14.3
ANALYSIS
OF
A
CATEGORICAL
AND
A
GAUSSIAN
TRAIT
.
615
14.3.1
SAMPLING
MODEL
.
616
14.3.2
PRIOR
DISTRIBUTION
AND
JOINT
POSTERIOR
DENSITY
.
.
.
617
14.3.3
FULLY
CONDITIONAL
POSTERIOR
DISTRIBUTIONS
.
619
14.3.4
THE
GIBBS
SAMPLER
.
625
14.3.5
IMPLEMENTATION
WITH
BINARY
TRAITS
.
626
15
BAYESIAN
ANALYSIS
OF
LONGITUDINAL
DATA
627
15.1
INTRODUCTION
.
627
15.2
HIERARCHICAL
OR
MULTISTAGE
MODELS
.
628
15.2.1
FIRST
STAGE
.
629
15.2.2
SECOND
STAGE
.
634
15.2.3
THIRD
STAGE
.
639
15.2.4
JOINT
POSTERIOR
DISTRIBUTION
.
641
15.3
TWO-STEP
APPROXIMATE
BAYESIAN
ANALYSIS
.
642
15.3.1
ESTIMATING
LOCATION
PARAMETERS
.
643
15.3.2
ESTIMATING
DISPERSION
PARAMETERS
.
650
15.3.3
SPECIAL
CASE:
LINEAR
FIRST
STAGE
.
652
15.4
COMPUTATION
VIA
MARKOV
CHAIN
MONTE
CARLO
.
653
15.4.1
FULLY
CONDITIONAL
POSTERIOR
DISTRIBUTIONS
.
655
15.5
ANALYSIS
WITH
THICK-TAILED
DISTRIBUTIONS
.
664
CONTENTS
XVII
15.5.1
FIRST
AND
SECOND-STAGE
MODELS
.
665
15.5.2
FULLY
CONDITIONAL
POSTERIOR
DISTRIBUTIONS
.
666
16
SEGREGATION
AND
QUANTITATIVE
TRAIT
LOCI
ANALYSIS
671
16.1
INTRODUCTION
.
671
16.2
SEGREGATION
ANALYSIS
MODELS
.
672
16.2.1
NOTATION
AND
MODEL
.
672
16.2.2
FULLY
CONDITIONAL
POSTERIOR
DISTRIBUTIONS
.
675
16.2.3
SOME
IMPLEMENTATION
ISSUES
.
677
16.3
QTL
MODELS
.
679
16.3.1
MODELS
WITH
A
SINGLE
QTL
.
680
16.3.2
MODELS
WITH
AN
ARBITRARY
NUMBER
OF
QTL
.
690
REFERENCES
701
LIST
OF
CITATIONS
727
SUBJECT
INDEX
733 |
| any_adam_object | 1 |
| author | Sorensen, Daniel |
| author_facet | Sorensen, Daniel |
| author_role | aut |
| author_sort | Sorensen, Daniel |
| author_variant | d s ds |
| building | Verbundindex |
| bvnumber | BV014380468 |
| callnumber-first | Q - Science |
| callnumber-label | QH438 |
| callnumber-raw | QH438.4.S73S675 2002 |
| callnumber-search | QH438.4.S73S675 2002 |
| callnumber-sort | QH 3438.4 S73 S675 42002 |
| callnumber-subject | QH - Natural History and Biology |
| classification_rvk | SK 830 SK 850 |
| classification_tum | BIO 180f BIO 107f |
| ctrlnum | (OCoLC)48907138 (DE-599)BVBBV014380468 |
| dewey-full | 575.6/07/27 575.6/07/2721 |
| dewey-hundreds | 500 - Natural sciences and mathematics |
| dewey-ones | 575 - Specific parts of & systems in plants |
| dewey-raw | 575.6/07/27 575.6/07/27 21 |
| dewey-search | 575.6/07/27 575.6/07/27 21 |
| dewey-sort | 3575.6 17 227 |
| dewey-tens | 570 - Biology |
| discipline | Biologie Mathematik |
| format | Book |
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| id | DE-604.BV014380468 |
| illustrated | Illustrated |
| indexdate | 2024-08-24T00:25:06Z |
| institution | BVB |
| isbn | 0387954406 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-009847898 |
| oclc_num | 48907138 |
| open_access_boolean | |
| owner | DE-91G DE-BY-TUM DE-521 DE-11 DE-20 |
| owner_facet | DE-91G DE-BY-TUM DE-521 DE-11 DE-20 |
| physical | XVII, 740 S. Ill. |
| publishDate | 2002 |
| publishDateSearch | 2002 |
| publishDateSort | 2002 |
| publisher | Springer-Verlag |
| record_format | marc |
| series2 | Statistics for biology and health |
| spelling | Sorensen, Daniel Verfasser aut Likelihood, Bayesian and MCMC methods in quantitative genetics Daniel Sorensen, Daniel Gianola New York Springer-Verlag 2002 XVII, 740 S. Ill. txt rdacontent n rdamedia nc rdacarrier Statistics for biology and health Includes bibliographical references (p. [701]-726) and index Genetica gtt Statistische methoden gtt Genetics -- Statistical methods Monte Carlo method Markov processes Bayesian statistical decision theory Genetik (DE-588)4071711-2 gnd rswk-swf Statistik (DE-588)4056995-0 gnd rswk-swf Statistik (DE-588)4056995-0 s Genetik (DE-588)4071711-2 s DE-604 Gianola, Daniel Sonstige oth DNB Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=009847898&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Sorensen, Daniel Likelihood, Bayesian and MCMC methods in quantitative genetics Genetica gtt Statistische methoden gtt Genetics -- Statistical methods Monte Carlo method Markov processes Bayesian statistical decision theory Genetik (DE-588)4071711-2 gnd Statistik (DE-588)4056995-0 gnd |
| subject_GND | (DE-588)4071711-2 (DE-588)4056995-0 |
| title | Likelihood, Bayesian and MCMC methods in quantitative genetics |
| title_auth | Likelihood, Bayesian and MCMC methods in quantitative genetics |
| title_exact_search | Likelihood, Bayesian and MCMC methods in quantitative genetics |
| title_full | Likelihood, Bayesian and MCMC methods in quantitative genetics Daniel Sorensen, Daniel Gianola |
| title_fullStr | Likelihood, Bayesian and MCMC methods in quantitative genetics Daniel Sorensen, Daniel Gianola |
| title_full_unstemmed | Likelihood, Bayesian and MCMC methods in quantitative genetics Daniel Sorensen, Daniel Gianola |
| title_short | Likelihood, Bayesian and MCMC methods in quantitative genetics |
| title_sort | likelihood bayesian and mcmc methods in quantitative genetics |
| topic | Genetica gtt Statistische methoden gtt Genetics -- Statistical methods Monte Carlo method Markov processes Bayesian statistical decision theory Genetik (DE-588)4071711-2 gnd Statistik (DE-588)4056995-0 gnd |
| topic_facet | Genetica Statistische methoden Genetics -- Statistical methods Monte Carlo method Markov processes Bayesian statistical decision theory Genetik Statistik |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=009847898&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT sorensendaniel likelihoodbayesianandmcmcmethodsinquantitativegenetics AT gianoladaniel likelihoodbayesianandmcmcmethodsinquantitativegenetics |