ライフサイエンスコーパス: likelihood function

1 fied in modern statistical approaches by the likelihood function.

2 ve behaviorally meaningful variations in the likelihood function.

3 of approximately factorizing the underlying likelihood function.

4 meters to approximate derivatives of the log likelihood function.

5 es is captured by a prior distribution and a likelihood function.

6 pare models without the need to evaluate the likelihood function.

7 We derive the estimator by maximizing the likelihood function.

8 roposal distribution and maximization of the likelihood function.

9 a posterior distribution depend on knowing a likelihood function.

10 ppropriate conditioning when calculating the likelihood function.

11 on and degradation that approximates the log-likelihood function.

12 ct value of mean deviance based on the exact likelihood function.

13 elling counts data using a negative binomial likelihood function.

14 The source of this bias is an inappropriate likelihood function.

15 rence for models associated with intractable likelihood functions.

16 activity of cortical neurons in the form of likelihood functions.

17 er optima with many fewer evaluations of the likelihood functions.

18 valents by using a hierarchical structure of likelihood functions.

19 ally limited by the set of models with known likelihood functions.

20 simplifications (i.e. factorizations of the likelihood function) adopted by certain abundance estima

21 model with alternative ways to calculate the likelihood function and (ii) allow sensitive selection o

22 ing efficient algorithms for calculating the likelihood function and searching for the maximum-likeli

23 r framework facilitates the use of realistic likelihood functions and enables systematic and genuine

24 k coalescent, characterize the corresponding likelihood function, and present efficient computational

25 hod uses a redescending penalty on the quasi-likelihood function, and thus has superior robustness ag

26 hods to maximize high-order multidimensional likelihood functions, and also offers the computation of

27 The analytical derivatives of the likelihood function are derived, thereby maximizing the

28 hastic and nonlinear dynamics, for which the likelihood functions are intractable.

29 other bioinformatics problems where complex likelihood functions are optimized.

30 hat uses the exact solution of an associated likelihood function as a prior probability distribution

31 We use a likelihood function based on an over-dispersed binomial

32 A likelihood function based on the L1-norm is adopted as i

33 We define a likelihood function based on the negative binomial distr

34 gically plausible model that can realize the likelihood function by computing a weighted sum of senso

35 nately, numerical computation of the DMN log-likelihood function by conventional methods results in i

36 ethod of QTL variance analysis maximizes the likelihood function by replacing the missing IBDs by the

37 The likelihood function can be worked out exactly for this m

38 e EM algorithm with an entropy penalized log-likelihood function (EPEM).

39 The method's likelihood function follows a binomial model of mRNA cap

40 of a gene family is given, we show that the likelihood function follows a multivariate normal distri

41 e a large number of parameters from a single-likelihood function for all genes.

42 ll-sequence by numerical maximization of the likelihood function for discrete-time Markov models.

43 The existing 'full ODP' requires that the likelihood function for each gene be evaluated according

44 l model for EMCCD noise properties, giving a likelihood function for image counts in each pixel for a

45 e and (2) by a strictly increasing composite likelihood function for the recombination parameter.

46 this Bayesian procedure is improved: (1) the likelihood function for the sequence has been modified t

47 ut infection times was incorporated into the likelihood function for the transmission history and phy

48 Likelihood functions for a given set of observations are

49 Often, they optimize likelihood functions for estimating model parameters, by

50 oaches were integrated into two mathematical likelihood functions for tumor classification.

51 We decoded the likelihood function from the trial-to-trial population a

52 o account by directly incorporating genotype likelihood function (GLF) of NGS data into association a

53 lts establish the role of population-encoded likelihood functions in mediating behavior and provide a

54 The joint likelihood function is composed of four component likeli

55 The correct likelihood function is derived and shown to be computati

56 The likelihood function is derived, assuming multivariate no

57 e unknown IBDs, a method to compute the full likelihood function is developed for families of arbitra

58 based maximum likelihood heuristic using its likelihood function is guaranteed to get stuck in a loca

59 Thus, our likelihood function is independent of those dynamics.

60 stacle for fitting such models is that their likelihood function is not explicitly available or is hi

61 alysis of complex stochastic models when the likelihood function is numerically unavailable.

62 A likelihood function is proposed for the discrete lengths

63 our novel method is based on an approximate likelihood function, it is highly flexible; we demonstra

64 Setting the methods in the context of the likelihood function L clarifies their underlying assumpt

65 ge analysis are shown to arise from a single likelihood function L for the observed allele-sharing da

66 First, we provide a formal likelihood function of actions (pro- and antisaccades) a

67 his distribution, we are able to compute the likelihood function of the number of segregating sites a

68 estricted maximum likelihood or the marginal likelihood function of the VC and identify its nontypica

69 The method uses the likelihood functions of Hartl et al. (1994) for inferenc

70 novel mathematical understanding of the log-likelihood function on the space of phylogenetic trees.

71 The Phylogenetic Likelihood Function (PLF) and its associated scaling and

72 tation, which includes both the phylogenetic likelihood function (PLF) and the tree likelihood calcul

73 ed to develop integrative theories, and that likelihood functions provide a common mathematical frame

74 the values of R(k)(t) maximizing a suitable likelihood function reproducing observed patterns of inf

75 expectation method), while in fact the full likelihood function should take into account the conditi

76 ver, these approximate factorizations of the likelihood function simplify calculations at the expense

77 ogramming algorithm that exactly optimizes a likelihood function specified by a probabilistic graphic

78 We formulated likelihood functions suitable for performing Bayesian UQ

79 s the commitment to a particular prior and a likelihood function that - in combination with Bayes' ru

80 Third, we introduce an approximate likelihood function that allows to estimate the location

81 function to approximate the Cox log-partial likelihood function that is stratified by site using pat

82 tion is used to derive model equations and a likelihood function that leads to an efficient computati

83 , our results depend on the formulation of a likelihood function that takes account of the generative

84 against filtering, agreement with a maximum likelihood function that takes into account experimental

85 As this does not require us to calculate the likelihood function, the model can be easily extended to

86 adopting an approximate factorization of the likelihood function they optimize.

87 riodic expression into a mixture-model-based likelihood function, thus producing results that are lik

88 This model includes a parameter in the likelihood function to account for the study population

89 This algorithm implements a surrogate likelihood function to approximate the Cox log-partial l

90 ProBound can leverage our threshold-free ASB likelihood function to perform de novo motif discovery u

91 ry heterogeneity and maximized the resulting likelihood functions to infer model parameters.

92 prior) with information from the study (the likelihood function) to generate an updated probability

93 CMs') generated climate scenarios (i.e., the likelihood function) to redefine the stochastic behavior

94 FFITHS and TAVARE is applied to estimate the likelihood function under different models of microsatel

95 Validation of the likelihood functions was performed on 265 public data se

96 oices for model parameters according to this likelihood function, we can then make probabilistic pred

97 Using derived likelihood functions, we statistically infer the mean an

98 eveals two major theoretical errors: (i) the likelihood function (which estimates the model parameter

99 omes the challenge of not having access to a likelihood function, which has severely limited inferenc

100 es sEM with an improved approximation to the likelihood function, which is unconstrained with regard

101 we derived and implemented the real maximum likelihood function, which turned out to provide us with

102 ESTIpop, parameter estimation is based on a likelihood function with respect to a time series of cel

103 we derive the analytical derivatives of the likelihood function with respect to all unknown model pa

104 ihood function is composed of four component likelihood functions with each of them derived from one