Assessment of techniques for cost-effectiveness of highway accident countermeasures. Final report.

Federal Highway Administration, Office of Research, Washington, D.C.

NASS investigator training - evaluation of the first training cycle. Final report.

National Highway Traffic Safety Administration, Washington, D.C.

Alcohol Safety Action Projects evaluation of operations: data, tables of results, and formulation. Volume 4.

Mode of access: Internet.

An analysis of total project impact on ultimate performance measures.

National Highway Traffic Safety Administration, Washington, D.C.

Computerized allocation of police traffic services; a demonstration study. Final report.

National Highway Traffic Safety Administration, Washington, D.C.

Analysis of project impact on ultimate performance measures. Analytical study no. 1. 1973.

National Highway Traffic Safety Administration, Washington, D.C.

Multidisciplinary accident investigation. Final report III.

National Highway Traffic Safety Administration, Washington, D.C.

Diagnostic motor vehicle inspection demonstration projects, program engineering support. Volume 8 - appendix G: vehicle-in-use standards. Final report.

National Highway Traffic Safety Administration, Washington, D.C.

Foremanship and accident prevention in industry.

Mode of access: Internet.

Dynamic, convex, and robust optimization with Bayesian learning for response-guided dosing

Thesis (Ph.D.)--University of Washington, 2016-08

A comparison of the efficacy and safety of olanzapine versus haloperidol during transition from intramuscular to oral therapy

Background: Acutely agitated patients with schizophrenia who receive intramuscular (IM) medications typically are switched to oral (PO) antipsychotic maintenance therapy Objective: The goal of this study was to assess the efficacy and safety of olanzapine versus those of haloperidol during transition from IM to PO therapy We used additional data from a previously reported trial to test the hypothesis that the reduction in agitation achieved by IM olanzapine 10 mg or IM haloperidol 7.5 mg would be maintained following transition to 4 days of PO olanzapine or PO haloperidol (5-20 mg/d for both). We also hypothesized that olanzapine would maintain its more favorable extrapyramidal symptom (EPS) safety profile. Methods: This was a multinational (hospitals in 13 countries), double-blind, randomized, controlled trial. Acutely agitated inpatients with schizophrenia were treated with 1 to 3 IM injections of olanzapine 10 mg or haloperidol 7.5 mg over 24 hours and were entered into a 4-day PO treatment period with the same medication (5-20 mg/d for both). The primary efficacy measurement was reduction in agitation, as measured by the Positive and Negative Syndrome Scale-Excited Component (PANSS-EC) score. Adverse events and scores on EPS rating scales were assessed. Results: A total of 311 patients (204 men, 107 women; mean [SD] age, 38.2 [11.6] years) were enrolled (131, 126, and 54 patients in the olanzapine, haloperidol, and placebo groups, respectively). In all, 93.1% (122/131) of olanzapine-treated patients and 92.1% (116/126) of haloperidol-treated patients completed the IM period and entered the PO period; 85.5% (112/131) of olanzapine-treated patients and 84.1% (106/126) of haloperidol-treated patients completed the PO period. IM olanzapine and IM haloperidol effectively reduced agitation over 24 hours (mean [SD] PANSS-EC change, -7.1 [4.8] vs -6.7 [4.3], respectively). Reductions in agitation were sustained throughout the PO period with both study drugs (mean [SD] change from PO period baseline, -0.6 [4.8] vs -1.3 [4.4], respectively). During PO treatment, haloperidol-treated patients spontaneously reported significantly more acute dystonia than olanzapine-treated patients (4.3% [5/116] vs 0% [0/122], respectively; P = 0.026) and akathisia (5.2% [6/116] vs 0% [0/122], respectively; P = 0.013). Significantly more haloperidol-treated patients than olanzapine-treated patients met categorical criteria for treatment-emergent akathisia (18.5% [17/92] vs 6.5% [7/107], respectively; P = 0.015). Conclusions: In the acutely agitated patients with schizophrenia in this study, both IM olanzapine 10 mg and IM haloperidol 7.5 mg effectively reduced agitation over 24 hours. This alleviation of agitation was sustained following transition from IM therapy to 4 days of PO treatment (5-20 mg/d for both). During the 4 days of PO treatment, olanzapine-treated patients did not spontaneously report any incidences of acute dystonia, and olanzapine had a superior EPS safety profile to that of haloperidol. The combination of IM and PO olanzapine may help improve the treatment of acutely agitated patients with schizophrenia. Copyright (C) 2003 Excerpta Medica, Inc.

Two Bayesian treatments of the n-tuple recognition method

Two probabilistic interpretations of the n-tuple recognition method are put forward in order to allow this technique to be analysed with the same Bayesian methods used in connection with other neural network models. Elementary demonstrations are then given of the use of maximum likelihood and maximum entropy methods for tuning the model parameters and assisting their interpretation. One of the models can be used to illustrate the significance of overlapping n-tuple samples with respect to correlations in the patterns.

Bayesian invariant measurements of generalisation

The problem of evaluating different learning rules and other statistical estimators is analysed. A new general theory of statistical inference is developed by combining Bayesian decision theory with information geometry. It is coherent and invariant. For each sample a unique ideal estimate exists and is given by an average over the posterior. An optimal estimate within a model is given by a projection of the ideal estimate. The ideal estimate is a sufficient statistic of the posterior, so practical learning rules are functions of the ideal estimator. If the sole purpose of learning is to extract information from the data, the learning rule must also approximate the ideal estimator. This framework is applicable to both Bayesian and non-Bayesian methods, with arbitrary statistical models, and to supervised, unsupervised and reinforcement learning schemes.

Using Bayesian neural networks to classify segmented images

We present results that compare the performance of neural networks trained with two Bayesian methods, (i) the Evidence Framework of MacKay (1992) and (ii) a Markov Chain Monte Carlo method due to Neal (1996) on a task of classifying segmented outdoor images. We also investigate the use of the Automatic Relevance Determination method for input feature selection.

Cascaded Bayesian processes: an account of bias in orientation perception

Following adaptation to an oriented (1-d) signal in central vision, the orientation of subsequently viewed test signals may appear repelled away from or attracted towards the adapting orientation. Small angular differences between the adaptor and test yield 'repulsive' shifts, while large angular differences yield 'attractive' shifts. In peripheral vision, however, both small and large angular differences yield repulsive shifts. To account for these tilt after-effects (TAEs), a cascaded model of orientation estimation that is optimized using hierarchical Bayesian methods is proposed. The model accounts for orientation bias through adaptation-induced losses in information that arise because of signal uncertainties and neural constraints placed upon the propagation of visual information. Repulsive (direct) TAEs arise at early stages of visual processing from adaptation of orientation-selective units with peak sensitivity at the orientation of the adaptor (theta). Attractive (indirect) TAEs result from adaptation of second-stage units with peak sensitivity at theta and theta+90 degrees , which arise from an efficient stage of linear compression that pools across the responses of the first-stage orientation-selective units. A spatial orientation vector is estimated from the transformed oriented unit responses. The change from attractive to repulsive TAEs in peripheral vision can be explained by the differing harmonic biases resulting from constraints on signal power (in central vision) versus signal uncertainties in orientation (in peripheral vision). The proposed model is consistent with recent work by computational neuroscientists in supposing that visual bias reflects the adjustment of a rational system in the light of uncertain signals and system constraints.