Markov Chain Monte Carlo constructs a Markov Chain (Xt) on Ω that converges to π, ie Pr[Xt = y|X0 = x]→ π(y) as t→∞, independent of x. Then we get a sampling algorithm by simulating the Markov chain, starting in an arbitrary state X0, for sufficiently many steps and outputting the final state Xt. It is usually not hard to set up a Markov chain that converges to the desired stationary distributi...

Markov chain sampling methods that automatically adapt to characteristics of the distribution being sampled can be constructed by exploiting the principle that one can sample from a distribution by sampling uniformly from the region under the plot of its density function. A Markov chain that converges to this uniform distribution can be constructed by alternating uniform sampling in the vertica...

Methods Prospective multicenter study of characteristics for repeated blood culture sampling in neonates admitted to three tertiary-referral intensive care units (period 7/201312/2014). Repeated blood culture samples were included when obtained within 14 days after the previous sample. Clinical sepsis is defined as the presence of 2 clinical signs and the duration of antibiotic therapy for ≥5 d...

Method: During a one-year period, 30 patients with Tourette disorder were studied at an adolescent psychiatry referral clinic. It was an analytical-descriptive study. Subjects were selected by convenience sampling. The control group was selected from students in Tehran schools similar to the first group in terms of age and gender. K-SADS questionnaire was used to examine the existence of obsess...

Kinematic loop-closure constraints significantly increase the difficulty of motion planning for articulated mechanisms. Configurations of closed-chain mechanisms do not form a single manifold, easy to parameterize, as the configurations of open kinematic chains. In general, they are grouped into several subsets with complex and a priori unknown topology. Sampling-based motion planning algorithm...

Stochastic billiards can be used for approximate sampling from the boundary of a bounded convex set through the Markov Chain Monte Carlo (MCMC) paradigm. This paper studies how many steps of the underlying Markov chain are required to get samples (approximately) from the uniform distribution on the boundary of the set, for sets with an upper bound on the curvature of the boundary. Our main theo...

results: the finding revealed that intention of family physicians to reduce referral rate of patients to specialists was significantly related to two theory-based variables of subjective norms (r = 0.38, p < 0.001) and perceived behavioral control (r = 0.43, p < 0.001), and not to attitudes. a stepwise regression entering direct measures of the theory variables explained 35% of the variance on ...

social capital is a multi-faceted phenomenon in social sciences that massively affects many social fields. it can be a helpful factor in promoting health. among the groups with high burnout, nurses have always shown higher levels of burnout. studies have revealed that social capital can be an important factor affecting burnout. this study aimed to determine the extent of the effect of social ca...

This chapter reviews the recent developments in Markov chain Monte Carlo simulation methods These methods, which are concerned with the simulation of high dimensional probability distributions, have gained enormous prominence and revolutionized Bayesian statistics The chapter provides background on the relevant Markov chain theory and provides detailed information on the theory and practice of ...

BACKGROUND Offering self-sampling to non-attendees of cervical screening increases screening attendance. METHODS We used observations from two Finnish studies on the use of self-sampling among the non-attendees to estimate in a hypothetical screening population of 100,000 women the possible costs per extra screened woman and costs per extra detected and treated CIN2+ with three intervention s...