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81.
Paul Sheridan Yuichi Yagahara Hidetoshi Shimodaira 《Annals of the Institute of Statistical Mathematics》2008,60(4):747-761
We propose a scale-free network model with a tunable power-law exponent. The Poisson growth model, as we call it, is an offshoot
of the celebrated model of Barabási and Albert where a network is generated iteratively from a small seed network; at each
step a node is added together with a number of incident edges preferentially attached to nodes already in the network. A key
feature of our model is that the number of edges added at each step is a random variable with Poisson distribution, and, unlike
the Barabási–Albert model where this quantity is fixed, it can generate any network. Our model is motivated by an application
in Bayesian inference implemented as Markov chain Monte Carlo to estimate a network; for this purpose, we also give a formula
for the probability of a network under our model. 相似文献
82.
Summary The aim of this paper is to propose new selection criteria for the orders of selfexciting threshold autoregressive (SETAR)
models. These criteria use bootstrap methodology; they are based on a weighted mean of the apparent error rate in the sample
and the average error rate obtained from bootstrap samples not containing the point being predicted. These new criteria are
compared with the traditional ones based on the Akaike information criterion (AIC). A simulation study and an example on a
real data set end the paper. 相似文献
83.
84.
Monica E. Bad Dumitrescu 《Annals of the Institute of Statistical Mathematics》1987,39(1):211-218
Summary The Bayesian estimation problem for the parameter θ of an exponential probability distribution is considered, when it is assumed
that θ has a natural conjugate prior density and a loss-function depending on the squared error is used. It is shown that,
with probability one, the posterior density of the Bayesian—centered and scaled parameter converges pointwise to the normal
probability density. The weak convergence of the posterior distributions to the normal distribution follows directly. Both
correct and incorrect models are studied and the asymptotic normality is stated respectively. 相似文献
85.
本文利用贝叶斯分析方法建立了评估企业诚信度的概率估计模型,并选取了一些有代表性的企业进行实证分析。与现有的同类问题研究相比,本模型的特点是将决策者个人经验和主观判断作为先验信息与样本信息相结合、将财务数据与诚信表现相结合,从而提高了估计的可靠性和准确性。 相似文献
86.
Probabilistic predictions with machine learning are important in many applications. These are commonly done with Bayesian learning algorithms. However, Bayesian learning methods are computationally expensive in comparison with non-Bayesian methods. Furthermore, the data used to train these algorithms are often distributed over a large group of end devices. Federated learning can be applied in this setting in a communication-efficient and privacy-preserving manner but does not include predictive uncertainty. To represent predictive uncertainty in federated learning, our suggestion is to introduce uncertainty in the aggregation step of the algorithm by treating the set of local weights as a posterior distribution for the weights of the global model. We compare our approach to state-of-the-art Bayesian and non-Bayesian probabilistic learning algorithms. By applying proper scoring rules to evaluate the predictive distributions, we show that our approach can achieve similar performance as the benchmark would achieve in a non-distributed setting. 相似文献
87.
Christina Petschnigg Markus Spitzner Lucas Weitzendorf Jürgen Pilz 《Entropy (Basel, Switzerland)》2021,23(3)
The 3D modelling of indoor environments and the generation of process simulations play an important role in factory and assembly planning. In brownfield planning cases, existing data are often outdated and incomplete especially for older plants, which were mostly planned in 2D. Thus, current environment models cannot be generated directly on the basis of existing data and a holistic approach on how to build such a factory model in a highly automated fashion is mostly non-existent. Major steps in generating an environment model of a production plant include data collection, data pre-processing and object identification as well as pose estimation. In this work, we elaborate on a methodical modelling approach, which starts with the digitalization of large-scale indoor environments and ends with the generation of a static environment or simulation model. The object identification step is realized using a Bayesian neural network capable of point cloud segmentation. We elaborate on the impact of the uncertainty information estimated by a Bayesian segmentation framework on the accuracy of the generated environment model. The steps of data collection and point cloud segmentation as well as the resulting model accuracy are evaluated on a real-world data set collected at the assembly line of a large-scale automotive production plant. The Bayesian segmentation network clearly surpasses the performance of the frequentist baseline and allows us to considerably increase the accuracy of the model placement in a simulation scene. 相似文献
88.
This paper states that most commonly used minimum divergence estimators are MLEs for suited generalized bootstrapped sampling schemes. Optimality in the sense of Bahadur for associated tests of fit under such sampling is considered. 相似文献
89.
We conduct a case study in which we empirically illustrate the performance of different classes of Bayesian inference methods to estimate stochastic volatility models. In particular, we consider how different particle filtering methods affect the variance of the estimated likelihood. We review and compare particle Markov Chain Monte Carlo (MCMC), RMHMC, fixed-form variational Bayes, and integrated nested Laplace approximation to estimate the posterior distribution of the parameters. Additionally, we conduct the review from the point of view of whether these methods are (1) easily adaptable to different model specifications; (2) adaptable to higher dimensions of the model in a straightforward way; (3) feasible in the multivariate case. We show that when using the stochastic volatility model for methods comparison, various data-generating processes have to be considered to make a fair assessment of the methods. Finally, we present a challenging specification of the multivariate stochastic volatility model, which is rarely used to illustrate the methods but constitutes an important practical application. 相似文献
90.
From a Bayesian analysis of the electric dipole polarizability,the constrained energy of isovector giant dipole resonance,the peak energy of isocalar giant quadrupole resonance,and the constrained energy of isocalar giant monopole resonance in ~(208)Pb,we extract the iso scalar and isovector effective masses in nuclear matter at saturation density p_0 as m_(s,0)~*/m=0.87_(-0.04)~(+0.04) and m_(v,0)~*/m=0.78_(-0.05)~(+0.06),respectively,at 90% confidence level.The constraints obtained on m_(s,0)~* and m_(v,0)~* lead to a positive iso spin splitting of nucleon effective mass in asymmetric nuclear matter of iso spin asymmetry δ at p_0 as m_(n-p)~*/m=(0.20_(-0.14)~(+0.15))δ.In addition,the symmetry energy at the subsaturation density p~*=0.05 fm~(-3) is determined to be E_(sym)(p~*)=16.7±1.3 MeV at 90% confidence level. 相似文献