Results 71 to 80 of about 167,711 (260)
Uncertainty Calibration in Molecular Machine Learning: Comparing Evidential and Ensemble Approaches
Chemistry – A European Journal, EarlyView.Raw uncertainty estimates from deep evidential regression and deep ensembles are systematically miscalibrated. Post hoc calibration aligns predicted uncertainty with true errors, improving reliability and enabling efficient active learning and reducing computational cost while preserving predictive accuracy.
Bidhan Chandra Garain +3 more
wiley +1 more source
Bayesian parameter inference by Markov chain Monte Carlo with hybrid fitness measures: theory and test in apoptosis signal transduction network. [PDF]
PLoS ONE, 2013 When model parameters in systems biology are not available from experiments, they need to be inferred so that the resulting simulation reproduces the experimentally known phenomena. For the purpose, Bayesian statistics with Markov chain Monte Carlo (MCMC)
Yohei Murakami, Shoji Takada
doaj +1 more source
Twenty years of dynamic occupancy models: a review of applications and look to the future
Ecography, EarlyView.Since their introduction over 20 years ago, dynamic occupancy models (DOMs) have become a powerful and flexible framework for estimating species occupancy across space and time while accounting for imperfect detection. As their popularity has increased and extensions have further expanded their capabilities, DOMs have been applied to increasingly ...
Saoirse Kelleher +3 more
wiley +1 more source
Why Bayesian Ideas Should Be Introduced in the Statistics Curricula and How to Do So
Journal of Statistics Education, 2020 While computing has become an important part of the statistics field, course offerings are still influenced by a legacy of mathematically centric thinking.
Andrew Hoegh
doaj +1 more source
Lattice Gaussian Sampling by Markov Chain Monte Carlo: Bounded Distance Decoding and Trapdoor Sampling [PDF]
, 2018 Sampling from the lattice Gaussian distribution plays an important role in various research fields. In this paper, the Markov chain Monte Carlo (MCMC)-based sampling technique is advanced in several fronts.
Ling, Cong, Wang, Zheng
core +1 more source
Ecography, EarlyView.The spatial ecology of stalk‐and‐ambush predators like the Eurasian lynx Lynx lynx depends on prey availability and environmental features, yet the relative roles of these factors remain unclear at large spatial scales. In this study, we analysed lynx habitat use across central and southern Finland using snow‐track data from the Wildlife Triangle ...
Francesca Malcangi +4 more
wiley +1 more source
On the Comovement of Contango and Backwardation Across Futures Commodity Markets
Journal of Futures Markets, EarlyView.ABSTRACT We examine the time‐varying nature of the comovement of the slope of the futures curve in major agricultural, metals and energy commodity futures markets in a Global Vector Autoregressive model. We find significant comovement between the slopes, indicating the co‐existence of backwardation and contango in many seemingly unrelated commodity ...
Angelo Luisi +2 more
wiley +1 more source
Model Probit Spasial pada Faktor-Faktor yang Mempengaruhi Klasifikasi IPM di Pulau Jawa
Cauchy: Jurnal Matematika Murni dan Aplikasi, 2013 Human pembagunan Index (HDI) is a composite index that includes three basic dimensions of human development is considered to reflect the status of the population's basic abilities of health, educational attainment, and purchasing power.
Feni Ira Puspita +2 more
doaj +1 more source
MCMC Using Hamiltonian Dynamics [PDF]
, 2011 Hamiltonian dynamics can be used to produce distant proposals for the Metropolis algorithm, thereby avoiding the slow exploration of the state space that results from the diffusive behaviour of simple random-walk proposals. Though originating in physics, Hamiltonian dynamics can be applied to most problems with continuous state spaces by simply ...
openaire +2 more sources
Augmentation schemes for particle MCMC [PDF]
Statistics and Computing, 2015 Particle MCMC involves using a particle filter within an MCMC algorithm. For inference of a model which involves an unobserved stochastic process, the standard implementation uses the particle filter to propose new values for the stochastic process, and MCMC moves to propose new values for the parameters.
Fearnhead, Paul, Meligkotsidou, Loukia
openaire +5 more sources