scholarly journals Structured priors in human forecasting

2018 ◽  
Author(s):  
Francisco Quiroga ◽  
Eric Schulz ◽  
Maarten Speekenbrink ◽  
Nigel Harvey
Keyword(s):  
Real World ◽  
Systematic Errors ◽  
Free Form ◽  
Real World Data ◽  
Data Set ◽  
Daily Lives ◽  
Situational Context ◽  
The Real ◽  
Systematic Biases ◽  
How People Learn

AbstractForecasting is an increasingly important part of our daily lives. Many studies on how people produce forecasts frame their behavior as prone to systematic errors. Based on recent evidence on how people learn about functions, we propose that participants’ forecasts are not irrational but rather driven by structured priors, i.e. situationally induced expectations of structure derived from experience with the real world. To test this, we extract participants’ priors over various contexts using a free-form forecasting paradigm. Instead of exhibiting systematic biases, our results show that participants’ priors match well with structure found in real-world data. Moreover, given the same data set, structured priors induce predictably different posterior forecasts depending on the evoked situational context.

scholarly journals Structured priors in human forecasting

2018 ◽  
Author(s):  
Francisco Quiroga ◽  
Eric Schulz ◽  
Maarten Speekenbrink ◽  
Nigel Harvey
Keyword(s):  
Real World ◽  
Systematic Errors ◽  
Free Form ◽  
Real World Data ◽  
Data Set ◽  
Daily Lives ◽  
Situational Context ◽  
The Real ◽  
Systematic Biases ◽  
How People Learn

Forecasting is an increasingly important part of our daily lives. Many studies on how people produce forecasts frame their behavior as prone to systematic errors. Based on recent evidence on how people learn about functions, we propose that participants' forecasts are not irrational but rather driven by structured priors, i.e. situationally induced expectations of structure derived from experience with the real world. To test this, we extract participants' priors over various contexts using a free-form forecasting paradigm. Instead of exhibiting systematic biases, our results show that participants' priors match well with structure found in real-world data. Moreover, given the same data set, structured priors induce predictably different posterior forecasts depending on the evoked situational context.

Adjusting the Outputs of a Classifier to New a Priori Probabilities: A Simple Procedure

2002 ◽  
Vol 14 (1) ◽  
pp. 21-41 ◽  
Author(s):  
Marco Saerens ◽  
Patrice Latinne ◽  
Christine Decaestecker
Keyword(s):  
Real World ◽  
Classification Accuracy ◽  
A Priori ◽  
Training Set ◽  
Real World Data ◽  
Data Set ◽  
World Data ◽  
The Real ◽  
Trained Classifier ◽  
A Priori Probability

It sometimes happens (for instance in case control studies) that a classifier is trained on a data set that does not reflect the true a priori probabilities of the target classes on real-world data. This may have a negative effect on the classification accuracy obtained on the real-world data set, especially when the classifier's decisions are based on the a posteriori probabilities of class membership. Indeed, in this case, the trained classifier provides estimates of the a posteriori probabilities that are not valid for this real-world data set (they rely on the a priori probabilities of the training set). Applying the classifier as is (without correcting its outputs with respect to these new conditions) on this new data set may thus be suboptimal. In this note, we present a simple iterative procedure for adjusting the outputs of the trained classifier with respect to these new a priori probabilities without having to refit the model, even when these probabilities are not known in advance. As a by-product, estimates of the new a priori probabilities are also obtained. This iterative algorithm is a straightforward instance of the expectation-maximization (EM) algorithm and is shown to maximize the likelihood of the new data. Thereafter, we discuss a statistical test that can be applied to decide if the a priori class probabilities have changed from the training set to the real-world data. The procedure is illustrated on different classification problems involving a multilayer neural network, and comparisons with a standard procedure for a priori probability estimation are provided. Our original method, based on the EM algorithm, is shown to be superior to the standard one for a priori probability estimation. Experimental results also indicate that the classifier with adjusted outputs always performs better than the original one in terms of classification accuracy, when the a priori probability conditions differ from the training set to the real-world data. The gain in classification accuracy can be significant.

scholarly journals Parameter Optimization for Real-World ENSO Forecast in an Intermediate Coupled Model

2019 ◽  
Vol 147 (5) ◽  
pp. 1429-1445 ◽  
Author(s):  
Yuchu Zhao ◽  
Zhengyu Liu ◽  
Fei Zheng ◽  
Yishuai Jin
Keyword(s):  
Data Assimilation ◽  
Parameter Optimization ◽  
Real World ◽  
Climate Models ◽  
Coupled Model ◽  
Prediction Skill ◽  
Model Parameters ◽  
Real World Data ◽  
The Real ◽  
Advection Term

Abstract We performed parameter estimation in the Zebiak–Cane model for the real-world scenario using the approach of ensemble Kalman filter (EnKF) data assimilation and the observational data of sea surface temperature and wind stress analyses. With real-world data assimilation in the coupled model, our study shows that model parameters converge toward stable values. Furthermore, the new parameters improve the real-world ENSO prediction skill, with the skill improved most by the parameter of the highest climate sensitivity (gam2), which controls the strength of anomalous upwelling advection term in the SST equation. The improved prediction skill is found to be contributed mainly by the improvement in the model dynamics, and second by the improvement in the initial field. Finally, geographic-dependent parameter optimization further improves the prediction skill across all the regions. Our study suggests that parameter optimization using ensemble data assimilation may provide an effective strategy to improve climate models and their real-world climate predictions in the future.

scholarly journals Trastuzumab-based palliative chemotherapy for HER2-positive gastric cancer: a single-center real-world data

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Tae-Hwan Kim ◽  
Hun Do Cho ◽  
Yong Won Choi ◽  
Hyun Woo Lee ◽  
Seok Yun Kang ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Real World ◽  
Group Performance ◽  
Eastern Cooperative Oncology Group ◽  
Conversion Surgery ◽  
Single Center ◽  
Her2 Positive ◽  
Real World Data ◽  
World Data ◽  
The Real

Abstract Background Since the results of the ToGA trial were published, trastuzumab-based chemotherapy has been used as the standard first-line treatment for HER2-positive recurrent or primary metastatic gastric cancer (RPMGC). However, the real-world data has been rarely reported. Therefore, we investigated the outcomes of trastuzumab-based chemotherapy in a single center. Methods This study analyzed the real-world data of 47 patients with HER2-positive RPMGC treated with trastuzumab-based chemotherapy in a single institution. Results With the median follow-up duration of 18.8 months in survivors, the median overall survival (OS) and progression-free survival were 12.8 and 6.9 months, respectively, and the overall response rate was 64%. Eastern Cooperative Oncology Group performance status 2 and massive amount of ascites were independent poor prognostic factors for OS, while surgical resection before or after chemotherapy was associated with favorable OS, in multivariate analysis. In addition, 5 patients who underwent conversion surgery after chemotherapy demonstrated an encouraging median OS of 30.8 months, all with R0 resection. Conclusions Trastuzumab-based chemotherapy in patients with HER2-positive RPMGC in the real world demonstrated outcomes almost comparable to those of the ToGA trial. Moreover, conversion surgery can be actively considered in fit patients with a favorable response after trastuzumab-based chemotherapy.

Kajian Literatur : Etnografi Digital Sebagai Cara Baru Dalam Pencarian Data Dalam Proses Perencanaan Arsitektur

AKSEN ◽  
2021 ◽  
Vol 6 (1) ◽  
pp. 19-31
Author(s):  
Andrey Caesar Effendi ◽  
LMF Purwanto
Keyword(s):  
Real World ◽  
Digital Technology ◽  
Ethnographic Methods ◽  
Daily Lives ◽  
The Real ◽  
Face To Face ◽  
Architectural Programming ◽  
Programming Process ◽  
The Way

The use of digital technology today can be said to be inseparable in our daily lives. Digital technology isslowly changing the way we communicate with others and the environment. Socialization that is usuallyface-to-face in the real world now can be done to not having to meet face-to-face in cyberspace. Thisliterature review aims to see a change in the way of obtaining data that is growing, with the use of digitaltechnology in ethnographic methods. The method used in this paper is to use descriptive qualitativeresearch methods by analyzing the existing literature. So it can be concluded that the use of digitalethnography in the architectural programming process can be a new way of searching for data at thearchitectural programming stage.

Surgery for metastases for esophageal-gastric cancer in the real world: Data from the AGAMENON national registry

2018 ◽  
Vol 44 (8) ◽  
pp. 1191-1198 ◽  
Author(s):  
Alberto Carmona-Bayonas ◽  
Paula Jiménez-Fonseca ◽  
Isabel Echavarria ◽  
Manuel Sánchez Cánovas ◽  
Gema Aguado ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Real World ◽  
National Registry ◽  
Real World Data ◽  
World Data ◽  
The Real

scholarly journals Validity of social media for assessing treatment patterns in oncology patients: a case study in melanoma

JAMIA Open ◽  
2019 ◽  
Vol 2 (4) ◽  
pp. 416-422
Author(s):  
Laura McDonald ◽  
Varun Behl ◽  
Vijayarakhavan Sundar ◽  
Faisal Mehmud ◽  
Bill Malcolm ◽  
...  
Keyword(s):  
Social Media ◽  
Confidence Intervals ◽  
Real World ◽  
Unmet Need ◽  
Data Access ◽  
Treatment Patterns ◽  
Patient Treatment ◽  
Real World Data ◽  
The Real ◽  
Third Line

Abstract There is a need to understand how patients are managed in the real world to better understand disease burden and unmet need. Traditional approaches to gather these data include the use of electronic medical record (EMR) or claims databases; however, in many cases data access policies prevent rapid insight gathering. Social media may provide a potential source of real-world data to assess treatment patterns, but the limitations and biases of doing so have not yet been evaluated. Here, we assessed whether patient treatment patterns extracted from publicly available patient forums compare to results from more traditional EMR and claims databases. We observed that the 95% confidence intervals of proportions of treatments received at first, second, and third line for advanced/metastatic melanoma generated from unstructured social media data overlapped with 95% confidence intervals from proportions obtained from 1 or more traditional EMR/Claims databases. Social media may offer a valid data option to understand treatment patterns in the real world.

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