Recursive Bayesian estimation using sequential Monte Carlos methods is a powerful numerical technique to understand latent dynamics of non-linear non-Gaussian dynamical systems. Classical sequential Monte Carlos suffer from weight degeneracy which is where the number of distinct particles collapse. Traditionally this is addressed by resampling, which effectively replaces high weight particles w...

Sequential effects are examined in four successive ratio estimation (RE) experiments. The procedure in successive RE is identical to that for magnitude estimation (ME), but the task in successive RE is to estimate the ratio of the current to the previous sensation magnitude, and not the separate magnitudes of the sensations. A positive stimulus context effect was found in successive RE for seve...

2 Group Sequential Designs (GSD) 2 2.1 GST object . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 2.1.1 Alpha spending function (SF) . . . . . . . . . . . . . . . . 4 2.2 plan.GST example . . . . . . . . . . . . . . . . . . . . . . . . . . 4 2.3 Overall p-values for GSDs . . . . . . . . . . . . . . . . . . . . . . 6 2.3.1 Repeated p-values in a classical GSD . . . . . . . . . . . ...

A new spacecraft attitude estimation approach using particle filtering is derived. Based on sequential Monte Carlo simulation, the particle filter approximately represents the probability distribution of the state vector with random samples. The filter formulation is based on the star camera measurements using a gyro-based or attitude dynamics-based model for attitude propagation. Modified Rodr...

Sequential Matching Estimation of Dynamic Causal Models This paper proposes sequential matching and inverse selection probability weighting to estimate dynamic causal effects. The sequential matching estimators extend simple, matching estimators based on propensity scores for static causal analysis that have been frequently applied in the evaluation literature. A Monte Carlo study shows that th...

In this paper we describe a metho.d. for estimating boundaries between percep~ually d1stmct regions in an image. The method 1S a two step procedure which first identifies image regions ~hich exhibit uniformity. Boundaries between umform regions are approximated by a complete cubic spline function and the linear recursive filter is used to estimate the values of the function at the knots. Bounda...

A power estimation approach is presented in which blocks of consecutive vectors are selected at random from a user-supplied realistic input vector set and the circuit is simulated for each block starting from an unknown state. This leads to two (upper and lower) bounds on the desired power value which can be quite tight (under 10% difference between the two in many cases). As a result, the powe...

W analyze the computational problem of estimating financial risk in a nested simulation. In this approach, an outer simulation is used to generate financial scenarios, and an inner simulation is used to estimate future portfolio values in each scenario. We focus on one risk measure, the probability of a large loss, and we propose a new algorithm to estimate this risk. Our algorithm sequentially...

We describe a compressive imager that adapts the measurement basis based on past measurements within a sequential Bayesian estimation framework. Simulation study shows a 7% improvement in reconstruction performance compared to a static measurement basis. © 2011 Optical Society of America OCIS codes: 110.1758,110.1085,100.3190.