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cleaned part one

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Kartik Madhira
2018-07-26 23:02:46 +05:30
parent f09075b61c
commit f328b2a477
1 changed files with 7 additions and 7 deletions
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Localization/Kalmanfilter_basics.ipynb
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@@ -4,8 +4,8 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"# KF Basics - Part I\n",
"\n"
"# _KF Basics - Part I_\n",
"----\n"
]
},
{
@@ -13,7 +13,7 @@
"metadata": {},
"source": [
"##### What is the need to describe belief in terms of PDF's?\n",
"This is because robot environments are stochastic. A robot environment may have cows with Tesla by side. That is a robot and it's environment cannot be deterministically modelled(e.g as a function of something like time t.). In the real world sensors are also error prone, and hence there'll be a set of values with a mean and variance that it can take. Hence, we always have to model around some mean and variances associated."
"This is because robot environments are stochastic. A robot environment may have cows with Tesla by side. That is a robot and it's environment cannot be deterministically modelled(e.g as a function of something like time t). In the real world sensors are also error prone, and hence there'll be a set of values with a mean and variance that it can take. Hence, we always have to model around some mean and variances associated."
]
},
{
@@ -72,7 +72,7 @@
"metadata": {},
"source": [
"## Variance, Covariance and Correlation\n",
"\n",
"----\n",
"### Variance\n",
"Variance is the spread of the data. The mean does'nt tell much **about** the data. Therefore the variance tells us about the **story** about the data meaning the spread of the data.\n",
"\n",
@@ -225,7 +225,7 @@
"metadata": {},
"source": [
"# Gaussians \n",
"\n",
"----\n",
"\n",
"\n",
"\n",
@@ -347,7 +347,7 @@
"metadata": {},
"source": [
"## Gaussian Properties\n",
"\n",
"----\n",
"**Multiplication**\n",
"\n",
"\n",
@@ -757,7 +757,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.6"
"version": "3.6.5"
}
},
"nbformat": 4,