Forecasting—the Bayesian approach

1983 ◽  
Vol 110 (01) ◽  
pp. 183-203
Author(s):  
R. J. Verrall
Keyword(s):  
Time Series ◽  
Bayesian Approach ◽  
Bayesian Forecasting ◽  
Statistical Forecasting ◽  
The Future ◽  
Data Points ◽  
The Bayesian Approach

For most people, statistical forecasting means modelling time series using the methods described by Box and Jenkins (1970). A Box and Jenkins model requires a large number of known data points before it can be properly chosen and, since it cannot be changed without again observing another batch of input, is only of use if the data conforms in the future to the chosen model. This means that this style of forecasting is essentially rigid, unadaptable and of limited use in practice. This paper sets out, with the aid of the examples, the essentials and some applications of Bayesian forecasting as developed by Harrison and Stevens (1976).

BAYESIAN EVALUATION OF THE CHANGE POINT IN THE SEQUENCES OF CORRELATED RANDOM VARIABLES OF HYDROMETEOROLOGICAL CHARACTERISTICS

2021 ◽  
pp. 27-35
Author(s):  
M. V. BOLGOV ◽  
Keyword(s):  
Time Series ◽  
Random Process ◽  
Bayesian Approach ◽  
Change Point ◽  
Marginal Distribution ◽  
Stationary States ◽  
Distribution Laws ◽  
Non Gaussian ◽  
Hydrometeorological Parameters ◽  
The Bayesian Approach

The paper considers the method for determining the point of change (the disturbance of stationarity) in the time series of hydrometeorological parameters characterized by a sequential change in stationary states of a random process. The method is based on the Bayesian approach to obtaining the distribution of the change point, which is generalized for a case of correlated sequences with non-Gaussian marginal distribution laws.

Bayesian Statistics in Sociology: Past, Present, and Future

2019 ◽  
Vol 45 (1) ◽  
pp. 47-68 ◽  
Author(s):  
Scott M. Lynch ◽  
Bryce Bartlett
Keyword(s):  
Social Science ◽  
Bayesian Statistics ◽  
Bayesian Approach ◽  
Bayesian Methods ◽  
Bayes Theorem ◽  
Widespread Application ◽  
Software Packages ◽  
The Future ◽  
Direct Use ◽  
The Bayesian Approach

Although Bayes’ theorem has been around for more than 250 years, widespread application of the Bayesian approach only began in statistics in 1990. By 2000, Bayesian statistics had made considerable headway into social science, but even now its direct use is rare in articles in top sociology journals, perhaps because of a lack of knowledge about the topic. In this review, we provide an overview of the key ideas and terminology of Bayesian statistics, and we discuss articles in the top journals that have used or developed Bayesian methods over the last decade. In this process, we elucidate some of the advantages of the Bayesian approach. We highlight that many sociologists are, in fact, using Bayesian methods, even if they do not realize it, because techniques deployed by popular software packages often involve Bayesian logic and/or computation. Finally, we conclude by briefly discussing the future of Bayesian statistics in sociology.

scholarly journals The Bayesian Approach and the Results of the ISAR-REACT 5 Trial

2021 ◽  
Vol 14 (2) ◽  
pp. 231-232
Author(s):  
Adnan Kastrati ◽  
Alexander Hapfelmeier
Keyword(s):  
Bayesian Approach ◽  
The Bayesian Approach

Analyses and anticipating the future trend of accidents in an electricity distribution company of Iran: A time series analysis

Work ◽  
2021 ◽  
pp. 1-6
Author(s):  
Shirin Nasrollah Nejhad ◽  
Tayebeh Ilaghinezhad Bardsiri ◽  
Maryam feiz arefi ◽  
Amin babaei poya ◽  
Ehsan mazloumi ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Occupational Injuries ◽  
Model Management ◽  
High Rate ◽  
Electricity Distribution ◽  
Temporary Workers ◽  
Series Analysis ◽  
The Future ◽  
Distribution Company

BACKGROUND: Many work-related fatalities happen every year in electricity distribution companies. This study was conducted to model accidents using the time series analysis and survey descriptive factors of injuries in an electricity distribution company in Tehran, Iran. METHODS: Data related to 2010 to 2017 were collected from the database of the safety department. Time Series and trend analysis were used for data analyzing and anticipating the accidents up to 2022. RESULT: Most of the accidents occurred in summer. Workers’ negligence was the reason for 75%of deaths. Employment type and type of injuries had a significant relationship (p <  0.05). CONCLUSION: The anticipating model indicated occupational injuries are going to have an increase in the future. A high rate of accidents in summer maybe because of the warm weather or insufficient skills in temporary workers. Temporary workers have no chance to work in a year like permanent workers, therefore acquisition experiences may be less in them. Based on the estimating model, Management should pay attention to those sectors of the company where most of the risky activities take place. Also, training programs and using personal protective equipment can help to protect workers in hazardous conditions.

Applied Bayesian Forecasting and Time Series Analysis.

1996 ◽  
Vol 45 (1) ◽  
pp. 136
Author(s):  
A. V. Metcalfe ◽  
A. Pole ◽  
M. West ◽  
J. Harrison
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Bayesian Forecasting ◽  
Series Analysis
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