没有合适的资源？快使用搜索试试~ 我知道了~

首页Sequential Monte Carlo Methods for Bayesian Computation

Sequential Monte Carlo Methods for Bayesian Computation

蒙特卡洛

贝叶斯

需积分: 12 15 下载量 125 浏览量更新于2023-03-16 评论 1 收藏 9.25MB PDF 举报

身份认证购VIP最低享 7 折!

领优惠券(最高得80元）

试读

407页

该文档主要是讲述蒙特卡洛在贝叶斯方面的应用，对于学习深度学习以及机器学习的人来说是个很好的资源

资源详情

资源评论

资源推荐

Sequential Monte Carlo Methods

for Bayesian Computation

A. Doucet

Kyoto

Sept. 2012

A . Doucet (MLSS Sept. 2012) Sept. 2012 1 / 136

Motivating Example 1: Generic Bayesian Model

Let X be a vector parameter of interest with an associated prior µ; i.e.

X  µ

(



)

We observe a realization of y of Y which is assumed to satisfy

(

X = x

)

 g

(



)

;

i.e. the likelihood function is g

(

)

Bayesian inference on X relies on the posterior of X given Y = y:

(

)

(

)

(

)

(

)

where the marginal likelihood/evidence satis…es

(

)

(

)

(

)

dx.

“Machine learning” examples: Latent Dirichlet Allocation,

(Hiearchical) Dirichlet processes...

A . Doucet (MLSS Sept. 2012) Sept. 2012 2 / 136

Motivating Example 1: Generic Bayesian Model

Let X be a vector parameter of interest with an associated prior µ; i.e.

X  µ

(



)

We observe a realization of y of Y which is assumed to satisfy

(

X = x

)

 g

(



)

;

i.e. the likelihood function is g

(

)

Bayesian inference on X relies on the posterior of X given Y = y:

(

)

(

)

(

)

(

)

where the marginal likelihood/evidence satis…es

(

)

(

)

(

)

dx.

“Machine learning” examples: Latent Dirichlet Allocation,

(Hiearchical) Dirichlet processes...

A . Doucet (MLSS Sept. 2012) Sept. 2012 2 / 136

Motivating Example 1: Generic Bayesian Model

Let X be a vector parameter of interest with an associated prior µ; i.e.

X  µ

(



)

We observe a realization of y of Y which is assumed to satisfy

(

X = x

)

 g

(



)

;

i.e. the likelihood function is g

(

)

Bayesian inference on X relies on the posterior of X given Y = y:

(

)

(

)

(

)

(

)

where the marginal likelihood/evidence satis…es

(

)

(

)

(

)

dx.

“Machine learning” examples: Latent Dirichlet Allocation,

(Hiearchical) Dirichlet processes...

A . Doucet (MLSS Sept. 2012) Sept. 2012 2 / 136

Motivating Example 1: Generic Bayesian Model

Let X be a vector parameter of interest with an associated prior µ; i.e.

X  µ

(



)

We observe a realization of y of Y which is assumed to satisfy

(

X = x

)

 g

(



)

;

i.e. the likelihood function is g

(

)

Bayesian inference on X relies on the posterior of X given Y = y:

(

)

(

)

(

)

(

)

where the marginal likelihood/evidence satis…es

(

)

(

)

(

)

dx.

“Machine learning” examples: Latent Dirichlet Allocation,

(Hiearchical) Dirichlet processes...

A . Doucet (MLSS Sept. 2012) Sept. 2012 2 / 136

剩余406页未读，继续阅读

You have trained a TensorFlow model using Dropout layers for regularization. Now, youwant to use Monte Carlo Dropout for making predictions. What do you need to dodifferently during the prediction stage?

To use Monte Carlo Dropout for making predictions, we need to modify the model by applying Dropout layers at prediction time. This can be done by setting the Dropout probability to zero during ...

考虑随机输入噪声的粒子滤波文献

On sequential Monte Carlo sampling methods for Bayesian filtering. Statistics and Computing, 10(3), 197-208. 2. Arulampalam, M. S., Maskell, S., Gordon, N. and Clapp, T. (2002). A tutorial on ...

3篇有关粒子算法的文献

3. 引用：Sequential Monte Carlo Methods in Practice by Christophe Andrieu, Arnaud Doucet, and Roman Holenstein (2010) 这本书是关于顺序蒙特卡洛方法（包括粒子滤波）在实践中的应用的权威参考。它涵盖了...

Python 粒子滤波

粒子滤波（Particle Filter）是一种用于状态估计的非参数滤波方法，常用于机器人定位和跟踪等领域。在Python中，可以使用... Sequential Monte Carlo methods in practice. Springer Science & Business Media. \[1\]

序贯蒙特卡洛python

序贯蒙特卡洛方法（Sequential Monte Carlo, SMC）是一种用于概率推断的方法，它通过逐步更新粒子集合来逼近目标分布。在Python中，可以使用一些库来实现序贯蒙特卡洛方法，例如`pymc3`和`pyro`。以下是使用`pymc3...

matlab优化算法 100例

44. Sequential Monte Carlo 45. Markov Chain Monte Carlo 46. Gibbs Sampling 47. Metropolis-Hastings Algorithm 48. Hamiltonian Monte Carlo 49. Variational Bayes 50. Expectation-Maximization Algorithm 51...

我现在有一个数据框df，变量有2个包括date、case，共204行数据，现在将前192行作为训练集，后12行作为测试集，使用LSTM模型对case进行时间序列预测，并使用蒙特卡洛 (Monte Carlo) Dropout得到训练集和测试集的置信区间，并将预测效果可视化。

from keras.models import Sequential from keras.layers import Dense, LSTM, Dropout from keras.callbacks import EarlyStopping, ModelCheckpoint import matplotlib.pyplot as plt # 读取数据 df = pd.read_...

序贯蒙特卡洛模拟matlab

在Matlab中，你可以使用序贯蒙特卡洛模拟（Sequential Monte Carlo Simulation）来估计复杂的概率分布或求解难以解地方程。这种方法通常用于高维问题或包含随机因素的系统建模。下面是一个简单的示例，演示了如何...

序贯蒙特卡洛算法smc

序贯蒙特卡洛算法（Sequential Monte Carlo algorithm，SMC）是一种用于处理过滤、平滑和参数估计等贝叶斯模型的推断算法。该算法通过蒙特卡洛采样的方式，逐步从先验分布逼近到后验分布。 SMC算法的核心思想是通过...

多目标跟踪算法matlab

GMS），非线性模型的扩展卡尔曼滤波（Extended Kalman Filter，EKF）、无迹卡尔曼滤波（Unscented Kalman Filter，UKF）和粒子滤波（Sequential Monte Carlo，SMC）解等。此外，在MATLAB中可以使用一些模型参数来...

nn.Sequential（nn.Sequential（），nn.Sequential（）），是不是等价于直接nn.Sequential（）

`nn.Sequential(nn.Sequential(), nn.Sequential())` 表示在外部创建了两个 `nn.Sequential` 容器，然后将它们作为两个独立的层添加到主 `nn.Sequential` 容器中。所以这个模型实际上有三个 `nn.Sequential` 层。 ...

torch.Sequential()

For example, the following code creates a simple neural network with two hidden layers using the Sequential class: ``` import torch.nn as nn model = nn.Sequential( nn.Linear(784, 256), nn.ReLU(), ...

self.actor = nn.Sequential

()The nn.Sequential module is used to create a sequential container for neural network layers. It allows us to stack multiple layers in a linear sequence to create a neural network model. In this ...

写一段python代码：用蒙特卡罗模拟场景，然后用神经网络进行训练，最后求得近似函数值

print("Monte Carlo simulation result:", monte_carlo_pi(1000000)) print("Neural network approximation result:", score[1]) ``` 这段代码首先生成了一些随机数据，并用蒙特卡罗模拟方法计算了 $\pi$ 的近似值...

def sequential(*args):

总之，def sequential(*args)是一个函数定义，它使用了特殊的*args参数来接收任意数量的参数，并在函数内部使用for循环对这些参数进行处理。 ### 回答2： `def sequential(*args):` 是一个函数的定义，其中 `*args...

nn.Sequential

It is commonly used for building neural networks, as it simplifies the process of creating complex models by allowing the user to specify the layers and operations in a sequential manner. In nn....

nn.Sequential()

nn.Sequential() is a PyTorch module that allows you to create a neural network by stacking up layers in a sequential manner. It is a container that holds modules (layers) in a specific order, where ...

Alladins

粉丝: 1
资源: 57

上传资源快速赚钱

我的内容管理收起

我的资源快来上传第一个资源

我的收益

登录查看自己的收益

我的积分登录查看自己的积分

我的C币登录后查看C币余额

我的收藏

我的下载

下载帮助

会员权益专享

Sequential Monte Carlo Methods for Bayesian Computation

评论0

会员权益专享

最新资源

Sequential Monte Carlo Methods for Bayesian Computation

评论0

Monte Carlo Methods in Bayesian Computation

Sequential Monte Carlo in C++

Monte Carlo Statistical Methods

You have trained a TensorFlow model using Dropout layers for regularization. Now, youwant to use Monte Carlo Dropout for making predictions. What do you need to dodifferently during the prediction stage?

考虑随机输入噪声的粒子滤波文献

3篇有关粒子算法的文献

Python 粒子滤波

序贯蒙特卡洛python

matlab优化算法 100例

序贯蒙特卡洛模拟matlab

序贯蒙特卡洛算法smc

多目标跟踪算法matlab

nn.Sequential（nn.Sequential（），nn.Sequential（）），是不是等价于直接nn.Sequential（）

torch.Sequential()

self.actor = nn.Sequential

写一段python代码：用蒙特卡罗模拟场景，然后用神经网络进行训练，最后求得近似函数值

def sequential(*args):

nn.Sequential

nn.Sequential()

会员权益专享

最新资源