A Bayesian Monte Carlo-based method for efficient computation of global sensitivity indices

Zhou, Yicheng; Cheng, Kai; Yun, Wanying

doi:10.1016/j.ymssp.2018.08.015

Cited by 59 publications

(14 citation statements)

References 27 publications

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“…But there is another side. Ethical limitations can significantly complicate the model, and since retraining is associated with the excessive complexity of the model used, this risk should be optimized by empirically measuring the probability of retraining (Monte Carlo method) [16].…”

Section: Resultsmentioning

confidence: 99%

Machine Learning Ethics in the Context of Justice Intuition

et al. 2019

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The article considers the ethics of machine learning in connection with such categories of social philosophy as justice, conviction, value. The ethics of machine learning is presented as a special case of a mathematical model of a dilemma -whether it corresponds to the "learning" algorithm of the intuition of justice or not. It has been established that the use of machine learning for decision making has a prospect only within the limits of the intuition of justice field based on fair algorithms. It is proposed to determine the effectiveness of the decision, considering the ethical component and given ethical restrictions. The cyclical nature of the relationship between the algorithmic algorithms subprocesses in machine learning and the stages of conducting mining analysis projects using the CRISP methodology has been established. The value of ethical constraints for each of the algorithmic processes has been determined. The provisions of the Theory of System Restriction are applied to find a way to measure the effect of ethical restrictions on the "learning" algorithm SHS Web of Conferences 69, 00150 (2019)

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Section: Resultsmentioning

confidence: 99%

Machine Learning Ethics in the Context of Justice Intuition

et al. 2019

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“…Monte Carlo juga berhasil mengeksploitasi dengan estimasi probabilitas kegagalan kecil dalam praktik teknik dengan memperkirakan indeks Sobol dengan biaya komputasi yang rendah dengan mengembangkan Aplikasi dengan strategi mentransformasikan evaluasi integral menjadi masalah inferensi Bayesian. Sehingga efisiensi dari metode yang baru dikembangkan ini sangat tinggi [19].…”

Section: Pendahuluanunclassified

Monte Carlo Prediksi Tingkat Prevalensi Stunting Kabupaten Lima Puluh Kota Menggunakan Metode Monte Carlo

Zaimy¹,

Defit

Nurcahyo

2021

jidt

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Stunting merupakan kondisi gagal tumbuh pada anak balita (bayi di bawah lima tahun) akibat dari kekurangan gizi kronis sehingga anak terlalu pendek untuk usianya. Menurut data yang ada, angka prevalensi stunting di Kabupaten Lima Puluh Kota tahun 2020 cukup tinggi yakni sebesar 8,28%. Hal ini menjadi perhatian pemerintah pusat dengan menetapkan Kabupaten Lima Puluh Kota sebagai salah satu Kabupaten/Kota Lokasi Fokus Intervensi Penurunan Stunting Terintegrasi Secara Nasional. Hasil penelitian ini bertujuan untuk membantu Pemerintah Kabupaten Lima Puluh Kota dalam perencanaan konvergensi program/intervensi sebagai salah satu upaya percepatan pencegahan stunting dan menurunkan persentase balita stunting di Kabupaten Lima Puluh Kota. Data penelitian ini menggunakan angka prevalensi stunting dari tahun 2018 sampai tahun 2020 yang berasal dari data jumlah balita dan jumlah balita stunting dari 22 puskesmas yang berada di Kabupaten Lima Puluh Kota. Selanjutnya data tersebut diolah menggunakan metode Monte Carlo untuk memprediksi tingkat prevalensi stunting tahun 2021. Berdasarkan pengujian yang dilakukan menggunakan metode Monte Carlo, didapatkan hasil tingkat prediksi stunting yang tertinggi berada pada puskesmas Pakan Rabaa dan puskesmas Suliki dengan angka prevalensi stunting sebesar 11,70%. Adapun tingkat akurasi yang diperoleh sebesar 93,73 %. Metode Monte Carlo cocok digunakan untuk prediksi angka prevalensi stunting di Kabupaten Lima Puluh Kota dilihat dari tingkat akurasinya yang cukup tinggi dari hasil pengolahan data.

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“…Monte Carlo Method (MCM) constructs a probabilistic model that approximates the performance of the system and performs random experiments on a digital computer [8]. It is very common to calculate the solid volume represented by the boundary with MCM [9,10].…”

Section: Introductionmentioning

confidence: 99%

Vertical tank capacity measurement based on Monte Carlo method

et al. 2021

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Vertical tanks are commonly used appliances for liquids, and its capacity is very important for quantitative liquid ratio and liquid trade. In order to measure the capacity of vertical tanks more conveniently, this paper proposes a vertical tank capacity measurement method based on Monte Carlo Method. The method arranges a plurality of sensor points on the inner surface of the tank, and then performs Monte Carlo tests by generating a large number of random sample points, and finally calculates the capacity by counting the sample points that meet the criterion. The criterion for whether a sample point is located in the tank, which is the core issue, is established with the coordinates of sensor points and the distance between different sensor points along the surface of the tank. The results show that the absolute error of the measurement results of the proposed method does not exceed ±0.0003[m3], and the absolute error of capacity per unit volume has a linear relationship with the height of the vertical tank, and has little effect with the radial size of the vertical tank.

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A Bayesian Monte Carlo-based method for efficient computation of global sensitivity indices

Cited by 59 publications

References 27 publications

Machine Learning Ethics in the Context of Justice Intuition

Machine Learning Ethics in the Context of Justice Intuition

Monte Carlo Prediksi Tingkat Prevalensi Stunting Kabupaten Lima Puluh Kota Menggunakan Metode Monte Carlo

Vertical tank capacity measurement based on Monte Carlo method

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