Results 101 to 110 of about 40,132 (204)
Markov Determinantal Point Process for Dynamic Random Sets
Journal of Time Series Analysis, EarlyView.ABSTRACT The Law of Determinantal Point Process (LDPP) is a flexible parametric family of distributions over random sets defined on a finite state space, or equivalently over multivariate binary variables. The aim of this paper is to introduce Markov processes of random sets within the LDPP framework. We show that, when the pairwise distribution of two
Christian Gouriéroux, Yang Lu
wiley +1 more source
The computation of case fatality rate for novel coronavirus (COVID-19) based on Bayes theorem: An observational study. [PDF]
Medicine (Baltimore), 2020 Chang CS +5 more
europepmc +1 more source
Multiple Chains Markov Switching Vector Autoregression
Journal of Time Series Analysis, EarlyView.ABSTRACT Both the U.S. stock and bond returns exhibit distinct Markovian regimes. However, because these regimes display limited coherence, conventional models typically require highly parameterized systems to adequately capture their joint distribution.
Leopoldo Catania
wiley +1 more source
Why Do Prosocial People Dislike Markets in Some Countries and Like Them in Others?
Kyklos, EarlyView.ABSTRACT Based on the doux commerce thesis, which suggests that people in market‐oriented societies hold stronger prosocial values than those in less market‐oriented ones, one can expect prosocial and pro‐market values to be positively associated. The fact that the association holds for cross‐country observations but does not universally hold for cross‐
Pál Czeglédi
wiley +1 more source
Rate of Convergence of Predictive Distributions for Dependent Data [PDF]
This paper deals with empirical processes of the type Cn(B) = n^(1/2) {µn(B) - P(Xn+1 in B | X1, . . . ,Xn)} , where (Xn) is a sequence of random variables and µn = (1/n)SUM(i=1,..,n) d(Xi) the empirical measure.
Pietro Rigo +3 more
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Mathematical Finance, EarlyView.ABSTRACT This study develops a novel multivariate stochastic framework for assessing systemic risks, such as climate and nature‐related shocks, within production or financial networks. By embedding a linear stochastic fluid network, interpretable as a generalized vector Ornstein–Uhlenbeck process, into the production network of interdependent ...
Giovanni Amici +3 more
wiley +1 more source
Bayesian and Related Methods: Techniques Based on Bayes\u27 Theorem
, 2012 Bayes\u27 theorem is a simple algebraic consequence of conditional probability. Yet, its consequences are critical to philosophy, society, and technology.
Vurkaç, Mehmet
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Aggregation and the Structure of Value
Noûs, EarlyView.ABSTRACT Roughly, the view I call “Additivism” sums up value across time and people. Given some standard assumptions, I show that Additivism follows from two principles. The first says that how lives align in time cannot, in itself, matter. The second says, roughly, that a world cannot be better unless it is better within some period or another.
Weng Kin San
wiley +1 more source
Bayes Theorem and Protopathic Bias: Methodological Concerns When Addressing the Impact of Fetal Heart Rate Patterns on the Cesarean Section Rate. [PDF]
AJP Rep, 2020 Balayla J +3 more
europepmc +1 more source
Comparing Bayes\u27s Theorem to Frequency-Based Approaches to Teaching Bayesian Reasoning
, 2003 Despite the conceptual simplicity of Bayesian reasoning, people often err when calculating or estimating conditional probability. These mistakes can have significant real-world consequences, and Bayes\u27s Theorem is a notoriously difficult remedy to ...
Ruscio, John
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