Results 51 to 60 of about 5,420,390 (236)

Bayesian Analysis of Aberrant Response and Response Time Data

Frontiers in Psychology, 2022
In this article, a highly effective Bayesian sampling algorithm based on auxiliary variables is proposed to analyze aberrant response and response time data.
Zhaoyuan Zhang, Zhaoyuan Zhang, Jiwei Zhang, Jing Lu   +3 more
doaj   +1 more source

A tutorial on Bayesian model averaging for exponential random graph models

British Journal of Mathematical and Statistical Psychology, EarlyView.
Abstract The use of exponential random graph models (ERGMs) is becoming prevalent in psychology due to their ability to explain and predict the formation of edges between vertices in a network. Valid inference with ERGMs requires correctly specifying endogenous and exogenous effects as network statistics, guided by theory, to represent the network ...
Ihnwhi Heo, Jan‐Willem Simons, Haiyan Liu   +2 more
wiley   +1 more source

Identifiability conditions in cognitive diagnosis: Implications for Q‐matrix estimation algorithms

British Journal of Mathematical and Statistical Psychology, EarlyView.
Abstract The Q‐matrix of a cognitively diagnostic assessment (CDA), documenting the item‐attribute associations, is a key component of any CDA. However, the true Q‐matrix underlying a CDA is never known and must be estimated—typically by content experts.
Hyunjoo Kim, Hans Friedrich Köhn, Chia‐Yi Chiu   +2 more
wiley   +1 more source

Bayesian inference for dynamic Q matrices and attribute trajectories in hidden Markov diagnostic classification models

British Journal of Mathematical and Statistical Psychology, EarlyView.
Abstract Hidden Markov diagnostic classification models capture how students' cognitive attributes evolve over time. This paper introduces a Bayesian Markov chain Monte Carlo algorithm for diagnostic classification models that jointly estimates time‐varying Q matrices, latent attributes, item parameters, attribute class proportions and transition ...
Chen‐Wei Liu
wiley   +1 more source

Probabilistic learning of nonlinear dynamical systems using sequential Monte Carlo

, 2017
Probabilistic modeling provides the capability to represent and manipulate uncertainty in data, models, predictions and decisions. We are concerned with the problem of learning probabilistic models of dynamical systems from measured data.
Lindsten, Fredrik, Murray, Lawrence, Schön, Thomas B., Svensson, Andreas   +3 more
core   +1 more source

Dutch disease, unemployment and structural change

Economic Inquiry, EarlyView.
Abstract We find that Dutch disease effects on unemployment are small even in a commodity‐rich economy like Australia. Using an estimated open‐economy model with frictional unemployment, we quantify how business‐cycle shocks and structural changes shape aggregate unemployment.
Mariano Kulish, James Morley, Nadine Yamout, Francesco Zanetti   +3 more
wiley   +1 more source

Unconstrained Metropolis–Hastings Sampling of Covariance Matrices

Journal of Probability and Statistics
Markov chain Monte Carlo (MCMC), the predominant algorithm for fitting hierarchal models to data in a Bayesian setting, relies on the ability to sample from the full conditional distributions of unobserved parameters.
Daniel Turek
doaj   +1 more source

Applying diffusion-based Markov chain Monte Carlo. [PDF]

PLoS ONE, 2017
We examine the performance of a strategy for Markov chain Monte Carlo (MCMC) developed by simulating a discrete approximation to a stochastic differential equation (SDE). We refer to the approach as diffusion MCMC.
Radu Herbei, Rajib Paul, L Mark Berliner
doaj   +1 more source

A Game-theoretic Formulation of the Homogeneous Self-Reconfiguration Problem [PDF]

, 2015
In this paper we formulate the homogeneous two- and three-dimensional self-reconfiguration problem over discrete grids as a constrained potential game. We develop a game-theoretic learning algorithm based on the Metropolis-Hastings algorithm that solves ...
Egerstedt, Magnus B., Egerstedt, Magnus B., Pickem, Daniel, Shamma, Jeff   +3 more
core   +2 more sources

Macroeconomic forecasting during recessions and expansions in the US and the euro area

Economic Inquiry, EarlyView.
Abstract This study systematically evaluates forecasting performance of 11 Dynamic Stochastic General Equilibrium (DSGE) and 2 Bayesian Vector Autoregression (BVAR) models during recessions and expansions in the US and the euro area. Results show that no single model dominates: parsimonious models perform well in stable periods and at short horizons ...
Jan Čapek, Jakub Chalmovianský, Vlastimil Reichel   +2 more
wiley   +1 more source